From bf8c0ec778f199b5fd69ca8a704e3ac867e28fd3 Mon Sep 17 00:00:00 2001 From: superstar54 Date: Tue, 1 Apr 2025 05:41:56 +0200 Subject: [PATCH 1/7] Use pyfunction --- aiida_qe.ipynb | 1850 ++++------------- .../src/python_workflow_definition/aiida.py | 136 +- 2 files changed, 418 insertions(+), 1568 deletions(-) diff --git a/aiida_qe.ipynb b/aiida_qe.ipynb index 07cb8d3..54f0cca 100644 --- a/aiida_qe.ipynb +++ b/aiida_qe.ipynb @@ -23,7 +23,7 @@ "from python_workflow_definition.aiida import write_workflow_json\n", "from python_workflow_definition.shared import get_dict, get_list\n", "\n", - "from aiida import load_profile\n", + "from aiida import load_profile, orm\n", "\n", "load_profile()\n", "\n", @@ -45,10 +45,10 @@ "metadata": {}, "outputs": [], "source": [ - "@task.pythonjob()\n", - "def pickle_node(value):\n", - " \"\"\"Handle data nodes\"\"\"\n", - " return value" + "from quantum_espresso_workflow import generate_structures\n", + "from quantum_espresso_workflow import get_bulk_structure\n", + "from quantum_espresso_workflow import calculate_qe as _calculate_qe\n", + "from quantum_espresso_workflow import plot_energy_volume_curve" ] }, { @@ -57,12 +57,7 @@ "metadata": {}, "outputs": [], "source": [ - "from quantum_espresso_workflow import generate_structures as _generate_structures\n", - "from quantum_espresso_workflow import get_bulk_structure as _get_bulk_structure\n", - "from quantum_espresso_workflow import calculate_qe as _calculate_qe\n", - "from quantum_espresso_workflow import (\n", - " plot_energy_volume_curve as _plot_energy_volume_curve,\n", - ")" + "calculate_qe = task(outputs=[\"energy\", \"volume\", \"structure\"])(_calculate_qe)" ] }, { @@ -70,27 +65,6 @@ "execution_count": 5, "metadata": {}, "outputs": [], - "source": [ - "strain_lst = [0.9, 0.95, 1.0, 1.05, 1.1]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "get_bulk_structure = task.pythonjob()(_get_bulk_structure)\n", - "generate_structures = task.pythonjob()(_generate_structures)\n", - "calculate_qe = task.pythonjob(outputs=[\"energy\", \"volume\", \"structure\"])(_calculate_qe)\n", - "plot_energy_volume_curve = task.pythonjob()(_plot_energy_volume_curve)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], "source": [ "wg = WorkGraph(\"wg-qe\")" ] @@ -99,121 +73,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Helper tasks that just pickle input data" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "pickle_element_task = wg.add_task(\n", - " pickle_node,\n", - " name=\"pickle_element\",\n", - " value=\"Al\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "pickle_a_task = wg.add_task(pickle_node, name=\"pickle_a\", value=4.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "pickle_cubic_task = wg.add_task(pickle_node, name=\"pickle_cubic\", value=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "pickle_relax_workdir_task = wg.add_task(\n", - " pickle_node,\n", - " name=\"pickle_relax_workdir\",\n", - " value=\"mini\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "pickle_pp_task = wg.add_task(\n", - " pickle_node,\n", - " name=\"pseudopotentials\",\n", - " value={\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "pickle_kpts_task = wg.add_task(pickle_node, name=\"kpts_task\", value=[3, 3, 3])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "pickle_calc_type_relax_task = wg.add_task(\n", - " pickle_node,\n", - " name=\"calc_type_relax\",\n", - " value=\"vc-relax\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "pickle_calc_type_scf_task = wg.add_task(\n", - " pickle_node,\n", - " name=\"calc_type_scf\",\n", - " value=\"scf\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "pickle_smearing_task = wg.add_task(pickle_node, name=\"smearing\", value=0.02)" + "## Prepare the inputs" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "pickle_strain_lst_task = wg.add_task(\n", - " pickle_node,\n", - " name=\"pickle_strain_lst\",\n", - " value=strain_lst,\n", - ")" + "element = orm.Str(\"Al\")\n", + "a = orm.Float(4.05)\n", + "cubic = orm.Bool(True)\n", + "relax_workdir = orm.Str(\"mini\")\n", + "pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n", + "kpts = orm.List([3, 3, 3])\n", + "calc_type_relax = orm.Str(\"vc-relax\")\n", + "calc_type_scf = orm.Str(\"scf\")\n", + "smearing = orm.Float(0.02)\n", + "strain_lst = orm.List([0.9, 0.95, 1.0, 1.05, 1.1])" ] }, { @@ -225,90 +103,78 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "get_bulk_structure_task = wg.add_task(\n", " get_bulk_structure,\n", " name=\"get_bulk_structure\",\n", - " register_pickle_by_value=True,\n", - " element=pickle_element_task.outputs.result,\n", - " a=pickle_a_task.outputs.result,\n", - " cubic=pickle_cubic_task.outputs.result,\n", + " element=element,\n", + " a=a,\n", + " cubic=cubic,\n", ")" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "relax_prepare_input_dict_task = wg.add_task(\n", - " task.pythonjob()(get_dict),\n", + " get_dict,\n", " name=\"relax_get_dict\",\n", - " register_pickle_by_value=True,\n", " structure=get_bulk_structure_task.outputs.result,\n", - " calculation=pickle_calc_type_relax_task.outputs.result,\n", - " kpts=pickle_kpts_task.outputs.result,\n", - " pseudopotentials=pickle_pp_task.outputs.result,\n", - " smearing=pickle_smearing_task.outputs.result,\n", + " calculation=calc_type_relax,\n", + " kpts=kpts,\n", + " pseudopotentials=pseudopotentials,\n", + " smearing=smearing,\n", ")\n", "\n", "relax_task = wg.add_task(\n", " calculate_qe,\n", " name=\"mini\",\n", - " register_pickle_by_value=True,\n", " input_dict=relax_prepare_input_dict_task.outputs.result,\n", - " working_directory=pickle_relax_workdir_task.outputs.result,\n", + " working_directory=relax_workdir,\n", ")" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "generate_structures_task = wg.add_task(\n", " generate_structures,\n", " name=\"generate_structures\",\n", - " register_pickle_by_value=True,\n", " structure=relax_task.outputs.structure,\n", - " strain_lst=pickle_strain_lst_task.outputs.result,\n", + " strain_lst=strain_lst,\n", ")" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "get_volumes_task = wg.add_task(\n", - " task.pythonjob()(get_list),\n", - " name=\"get_volumes\",\n", - " register_pickle_by_value=True,\n", - ")" + "get_volumes_task = wg.add_task(get_list, name=\"get_volumes\")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "get_energies_task = wg.add_task(\n", - " task.pythonjob()(get_list),\n", - " name=\"get_energies\",\n", - " register_pickle_by_value=True,\n", - ")" + "get_energies_task = wg.add_task(get_list, name=\"get_energies\")" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -317,33 +183,24 @@ "for i, strain in enumerate(strain_lst):\n", "\n", " structure_key = f\"s_{i}\"\n", - " strain_dir = f\"strain_{i}\"\n", + " strain_dir = orm.Str(f\"strain_{i}\")\n", " generate_structures_task.add_output(\"workgraph.any\", structure_key)\n", "\n", - " strain_dir_task = wg.add_task(\n", - " pickle_node,\n", - " name=f\"pickle_{strain_dir}_dir\",\n", - " value=strain_dir,\n", - " register_pickle_by_value=True,\n", - " )\n", - "\n", " scf_prepare_input_dict_task = wg.add_task(\n", - " task.pythonjob()(get_dict),\n", + " get_dict,\n", " name=f\"get_dict_{i}\",\n", - " register_pickle_by_value=True,\n", " structure=generate_structures_task.outputs[structure_key],\n", - " calculation=pickle_calc_type_scf_task.outputs.result,\n", - " kpts=pickle_kpts_task.outputs.result,\n", - " pseudopotentials=pickle_pp_task.outputs.result,\n", - " smearing=pickle_smearing_task.outputs.result,\n", + " calculation=calc_type_scf,\n", + " kpts=kpts,\n", + " pseudopotentials=pseudopotentials,\n", + " smearing=smearing,\n", " )\n", "\n", " scf_qe_task = wg.add_task(\n", " calculate_qe,\n", " name=f\"qe_{i}\",\n", - " register_pickle_by_value=True,\n", " input_dict=scf_prepare_input_dict_task.outputs.result,\n", - " working_directory=strain_dir_task.outputs.result,\n", + " working_directory=strain_dir,\n", " )\n", "\n", " # collect energy and volume\n", @@ -353,14 +210,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "plot_energy_volume_curve_task = wg.add_task(\n", " plot_energy_volume_curve,\n", " name=\"plot_energy_volume_curve\",\n", - " register_pickle_by_value=True,\n", " volume_lst=get_volumes_task.outputs.result,\n", " energy_lst=get_energies_task.outputs.result,\n", ")" @@ -368,13 +224,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cce51ee9902841d79d1f53655cd939bc", + "model_id": "d15bd7f73ab14488b65fa5997453b4bd", "version_major": 2, "version_minor": 1 }, @@ -382,7 +238,7 @@ "NodeGraphWidget(settings={'minimap': True}, style={'width': '90%', 'height': '600px'}, value={'name': 'wg-qe',…" ] }, - "execution_count": 25, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -393,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -402,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -411,405 +267,406 @@ "text": [ "{\n", " \"nodes\": {\n", - " \"0\": \"Al\",\n", - " \"1\": 4.05,\n", - " \"2\": true,\n", - " \"3\": \"mini\",\n", - " \"4\": {\n", - " \"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"\n", - " },\n", - " \"5\": [\n", + " \"0\": \"quantum_espresso_workflow.get_bulk_structure\",\n", + " \"1\": \"python_workflow_definition.shared.get_dict\",\n", + " \"2\": \"quantum_espresso_workflow.calculate_qe\",\n", + " \"3\": \"quantum_espresso_workflow.generate_structures\",\n", + " \"4\": \"python_workflow_definition.shared.get_list\",\n", + " \"5\": \"python_workflow_definition.shared.get_list\",\n", + " \"6\": \"python_workflow_definition.shared.get_dict\",\n", + " \"7\": \"quantum_espresso_workflow.calculate_qe\",\n", + " \"8\": \"python_workflow_definition.shared.get_dict\",\n", + " \"9\": \"quantum_espresso_workflow.calculate_qe\",\n", + " \"10\": \"python_workflow_definition.shared.get_dict\",\n", + " \"11\": \"quantum_espresso_workflow.calculate_qe\",\n", + " \"12\": \"python_workflow_definition.shared.get_dict\",\n", + " \"13\": \"quantum_espresso_workflow.calculate_qe\",\n", + " \"14\": \"python_workflow_definition.shared.get_dict\",\n", + " \"15\": \"quantum_espresso_workflow.calculate_qe\",\n", + " \"16\": \"quantum_espresso_workflow.plot_energy_volume_curve\",\n", + " \"17\": \"Al\",\n", + " \"18\": 4.05,\n", + " \"19\": true,\n", + " \"20\": \"vc-relax\",\n", + " \"21\": [\n", " 3,\n", " 3,\n", " 3\n", " ],\n", - " \"6\": \"vc-relax\",\n", - " \"7\": \"scf\",\n", - " \"8\": 0.02,\n", - " \"9\": [\n", + " \"22\": {\n", + " \"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\",\n", + " \"node_type\": \"data.core.dict.Dict.\"\n", + " },\n", + " \"23\": 0.02,\n", + " \"24\": \"mini\",\n", + " \"25\": [\n", " 0.9,\n", " 0.95,\n", " 1.0,\n", " 1.05,\n", " 1.1\n", " ],\n", - " \"10\": \"quantum_espresso_workflow.get_bulk_structure\",\n", - " \"11\": \"python_workflow_definition.shared.get_dict\",\n", - " \"12\": \"quantum_espresso_workflow.calculate_qe\",\n", - " \"13\": \"quantum_espresso_workflow.generate_structures\",\n", - " \"14\": \"python_workflow_definition.shared.get_list\",\n", - " \"15\": \"python_workflow_definition.shared.get_list\",\n", - " \"16\": \"strain_0\",\n", - " \"17\": \"python_workflow_definition.shared.get_dict\",\n", - " \"18\": \"quantum_espresso_workflow.calculate_qe\",\n", - " \"19\": \"strain_1\",\n", - " \"20\": \"python_workflow_definition.shared.get_dict\",\n", - " \"21\": \"quantum_espresso_workflow.calculate_qe\",\n", - " \"22\": \"strain_2\",\n", - " \"23\": \"python_workflow_definition.shared.get_dict\",\n", - " \"24\": \"quantum_espresso_workflow.calculate_qe\",\n", - " \"25\": \"strain_3\",\n", - " \"26\": \"python_workflow_definition.shared.get_dict\",\n", - " \"27\": \"quantum_espresso_workflow.calculate_qe\",\n", - " \"28\": \"strain_4\",\n", - " \"29\": \"python_workflow_definition.shared.get_dict\",\n", - " \"30\": \"quantum_espresso_workflow.calculate_qe\",\n", - " \"31\": \"quantum_espresso_workflow.plot_energy_volume_curve\"\n", + " \"26\": \"scf\",\n", + " \"27\": \"strain_0\",\n", + " \"28\": \"strain_1\",\n", + " \"29\": \"strain_2\",\n", + " \"30\": \"strain_3\",\n", + " \"31\": \"strain_4\"\n", " },\n", " \"edges\": [\n", " {\n", - " \"target\": 10,\n", - " \"targetHandle\": \"element\",\n", - " \"source\": 0,\n", - " \"sourceHandle\": null\n", + " \"tn\": 1,\n", + " \"th\": \"structure\",\n", + " \"sn\": 0,\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 10,\n", - " \"targetHandle\": \"a\",\n", - " \"source\": 1,\n", - " \"sourceHandle\": null\n", + " \"tn\": 2,\n", + " \"th\": \"input_dict\",\n", + " \"sn\": 1,\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 10,\n", - " \"targetHandle\": \"cubic\",\n", - " \"source\": 2,\n", - " \"sourceHandle\": null\n", + " \"tn\": 3,\n", + " \"th\": \"structure\",\n", + " \"sn\": 2,\n", + " \"sh\": \"structure\"\n", " },\n", " {\n", - " \"target\": 11,\n", - " \"targetHandle\": \"structure\",\n", - " \"source\": 10,\n", - " \"sourceHandle\": null\n", + " \"tn\": 6,\n", + " \"th\": \"structure\",\n", + " \"sn\": 3,\n", + " \"sh\": \"s_0\"\n", " },\n", " {\n", - " \"target\": 11,\n", - " \"targetHandle\": \"calculation\",\n", - " \"source\": 6,\n", - " \"sourceHandle\": null\n", + " \"tn\": 7,\n", + " \"th\": \"input_dict\",\n", + " \"sn\": 6,\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 11,\n", - " \"targetHandle\": \"kpts\",\n", - " \"source\": 5,\n", - " \"sourceHandle\": null\n", + " \"tn\": 5,\n", + " \"th\": \"0\",\n", + " \"sn\": 7,\n", + " \"sh\": \"energy\"\n", " },\n", " {\n", - " \"target\": 11,\n", - " \"targetHandle\": \"pseudopotentials\",\n", - " \"source\": 4,\n", - " \"sourceHandle\": null\n", + " \"tn\": 4,\n", + " \"th\": \"0\",\n", + " \"sn\": 7,\n", + " \"sh\": \"volume\"\n", " },\n", " {\n", - " \"target\": 11,\n", - " \"targetHandle\": \"smearing\",\n", - " \"source\": 8,\n", - " \"sourceHandle\": null\n", + " \"tn\": 8,\n", + " \"th\": \"structure\",\n", + " \"sn\": 3,\n", + " \"sh\": \"s_1\"\n", " },\n", " {\n", - " \"target\": 12,\n", - " \"targetHandle\": \"input_dict\",\n", - " \"source\": 11,\n", - " \"sourceHandle\": null\n", + " \"tn\": 9,\n", + " \"th\": \"input_dict\",\n", + " \"sn\": 8,\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 12,\n", - " \"targetHandle\": \"working_directory\",\n", - " \"source\": 3,\n", - " \"sourceHandle\": null\n", + " \"tn\": 5,\n", + " \"th\": \"1\",\n", + " \"sn\": 9,\n", + " \"sh\": \"energy\"\n", " },\n", " {\n", - " \"target\": 13,\n", - " \"targetHandle\": \"structure\",\n", - " \"source\": 12,\n", - " \"sourceHandle\": \"structure\"\n", + " \"tn\": 4,\n", + " \"th\": \"1\",\n", + " \"sn\": 9,\n", + " \"sh\": \"volume\"\n", " },\n", " {\n", - " \"target\": 13,\n", - " \"targetHandle\": \"strain_lst\",\n", - " \"source\": 9,\n", - " \"sourceHandle\": null\n", + " \"tn\": 10,\n", + " \"th\": \"structure\",\n", + " \"sn\": 3,\n", + " \"sh\": \"s_2\"\n", " },\n", " {\n", - " \"target\": 17,\n", - " \"targetHandle\": \"structure\",\n", - " \"source\": 13,\n", - " \"sourceHandle\": \"s_0\"\n", + " \"tn\": 11,\n", + " \"th\": \"input_dict\",\n", + " \"sn\": 10,\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 17,\n", - " \"targetHandle\": \"calculation\",\n", - " \"source\": 7,\n", - " \"sourceHandle\": null\n", + " \"tn\": 5,\n", + " \"th\": \"2\",\n", + " \"sn\": 11,\n", + " \"sh\": \"energy\"\n", " },\n", " {\n", - " \"target\": 17,\n", - " \"targetHandle\": \"kpts\",\n", - " \"source\": 5,\n", - " \"sourceHandle\": null\n", + " \"tn\": 4,\n", + " \"th\": \"2\",\n", + " \"sn\": 11,\n", + " \"sh\": \"volume\"\n", " },\n", " {\n", - " \"target\": 17,\n", - " \"targetHandle\": \"pseudopotentials\",\n", - " \"source\": 4,\n", - " \"sourceHandle\": null\n", + " \"tn\": 12,\n", + " \"th\": \"structure\",\n", + " \"sn\": 3,\n", + " \"sh\": \"s_3\"\n", " },\n", " {\n", - " \"target\": 17,\n", - " \"targetHandle\": \"smearing\",\n", - " \"source\": 8,\n", - " \"sourceHandle\": null\n", + " \"tn\": 13,\n", + " \"th\": \"input_dict\",\n", + " \"sn\": 12,\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 18,\n", - " \"targetHandle\": \"input_dict\",\n", - " \"source\": 17,\n", - " \"sourceHandle\": null\n", + " \"tn\": 5,\n", + " \"th\": \"3\",\n", + " \"sn\": 13,\n", + " \"sh\": \"energy\"\n", " },\n", " {\n", - " \"target\": 18,\n", - " \"targetHandle\": \"working_directory\",\n", - " \"source\": 16,\n", - " \"sourceHandle\": null\n", + " \"tn\": 4,\n", + " \"th\": \"3\",\n", + " \"sn\": 13,\n", + " \"sh\": \"volume\"\n", " },\n", " {\n", - " \"target\": 15,\n", - " \"targetHandle\": \"0\",\n", - " \"source\": 18,\n", - " \"sourceHandle\": \"energy\"\n", + " \"tn\": 14,\n", + " \"th\": \"structure\",\n", + " \"sn\": 3,\n", + " \"sh\": \"s_4\"\n", " },\n", " {\n", - " \"target\": 14,\n", - " \"targetHandle\": \"0\",\n", - " \"source\": 18,\n", - " \"sourceHandle\": \"volume\"\n", + " \"tn\": 15,\n", + " \"th\": \"input_dict\",\n", + " \"sn\": 14,\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 20,\n", - " \"targetHandle\": \"structure\",\n", - " \"source\": 13,\n", - " \"sourceHandle\": \"s_1\"\n", + " \"tn\": 5,\n", + " \"th\": \"4\",\n", + " \"sn\": 15,\n", + " \"sh\": \"energy\"\n", " },\n", " {\n", - " \"target\": 20,\n", - " \"targetHandle\": \"calculation\",\n", - " \"source\": 7,\n", - " \"sourceHandle\": null\n", + " \"tn\": 4,\n", + " \"th\": \"4\",\n", + " \"sn\": 15,\n", + " \"sh\": \"volume\"\n", " },\n", " {\n", - " \"target\": 20,\n", - " \"targetHandle\": \"kpts\",\n", - " \"source\": 5,\n", - " \"sourceHandle\": null\n", + " \"tn\": 16,\n", + " \"th\": \"volume_lst\",\n", + " \"sn\": 4,\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 20,\n", - " \"targetHandle\": \"pseudopotentials\",\n", - " \"source\": 4,\n", - " \"sourceHandle\": null\n", + " \"tn\": 16,\n", + " \"th\": \"energy_lst\",\n", + " \"sn\": 5,\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 20,\n", - " \"targetHandle\": \"smearing\",\n", - " \"source\": 8,\n", - " \"sourceHandle\": null\n", + " \"tn\": 0,\n", + " \"th\": \"element\",\n", + " \"sn\": \"17\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 21,\n", - " \"targetHandle\": \"input_dict\",\n", - " \"source\": 20,\n", - " \"sourceHandle\": null\n", + " \"tn\": 0,\n", + " \"th\": \"a\",\n", + " \"sn\": \"18\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 21,\n", - " \"targetHandle\": \"working_directory\",\n", - " \"source\": 19,\n", - " \"sourceHandle\": null\n", + " \"tn\": 0,\n", + " \"th\": \"cubic\",\n", + " \"sn\": \"19\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 15,\n", - " \"targetHandle\": \"1\",\n", - " \"source\": 21,\n", - " \"sourceHandle\": \"energy\"\n", + " \"tn\": 1,\n", + " \"th\": \"calculation\",\n", + " \"sn\": \"20\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 14,\n", - " \"targetHandle\": \"1\",\n", - " \"source\": 21,\n", - " \"sourceHandle\": \"volume\"\n", + " \"tn\": 1,\n", + " \"th\": \"kpts\",\n", + " \"sn\": \"21\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 23,\n", - " \"targetHandle\": \"structure\",\n", - " \"source\": 13,\n", - " \"sourceHandle\": \"s_2\"\n", + " \"tn\": 1,\n", + " \"th\": \"pseudopotentials\",\n", + " \"sn\": \"22\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 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\"target\": 29,\n", - " \"targetHandle\": \"structure\",\n", - " \"source\": 13,\n", - " \"sourceHandle\": \"s_4\"\n", + " \"tn\": 11,\n", + " \"th\": \"working_directory\",\n", + " \"sn\": \"29\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 29,\n", - " \"targetHandle\": \"calculation\",\n", - " \"source\": 7,\n", - " \"sourceHandle\": null\n", + " \"tn\": 12,\n", + " \"th\": \"calculation\",\n", + " \"sn\": \"26\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 29,\n", - " \"targetHandle\": \"kpts\",\n", - " \"source\": 5,\n", - " \"sourceHandle\": null\n", + " \"tn\": 12,\n", + " \"th\": \"kpts\",\n", + " \"sn\": \"21\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 29,\n", - " \"targetHandle\": \"pseudopotentials\",\n", - " \"source\": 4,\n", - " \"sourceHandle\": null\n", + " \"tn\": 12,\n", + " \"th\": \"pseudopotentials\",\n", + " \"sn\": \"22\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 29,\n", - " \"targetHandle\": \"smearing\",\n", - " \"source\": 8,\n", - " \"sourceHandle\": null\n", + " \"tn\": 12,\n", + " \"th\": \"smearing\",\n", + " \"sn\": \"23\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 30,\n", - " \"targetHandle\": \"input_dict\",\n", - " \"source\": 29,\n", - " \"sourceHandle\": null\n", + " \"tn\": 13,\n", + " \"th\": \"working_directory\",\n", + " \"sn\": \"30\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 30,\n", - " \"targetHandle\": \"working_directory\",\n", - " \"source\": 28,\n", - " \"sourceHandle\": null\n", + " \"tn\": 14,\n", + " \"th\": \"calculation\",\n", + " \"sn\": \"26\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 15,\n", - " \"targetHandle\": \"4\",\n", - " \"source\": 30,\n", - " \"sourceHandle\": \"energy\"\n", + " \"tn\": 14,\n", + " \"th\": \"kpts\",\n", + " \"sn\": \"21\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 14,\n", - " \"targetHandle\": \"4\",\n", - " \"source\": 30,\n", - " \"sourceHandle\": \"volume\"\n", + " \"tn\": 14,\n", + " \"th\": \"pseudopotentials\",\n", + " \"sn\": \"22\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 31,\n", - " \"targetHandle\": \"volume_lst\",\n", - " \"source\": 14,\n", - " \"sourceHandle\": null\n", + " \"tn\": 14,\n", + " \"th\": \"smearing\",\n", + " \"sn\": \"23\",\n", + " \"sh\": null\n", " },\n", " {\n", - " \"target\": 31,\n", - " \"targetHandle\": \"energy_lst\",\n", - " \"source\": 15,\n", - " \"sourceHandle\": null\n", + " \"tn\": 15,\n", + " \"th\": \"working_directory\",\n", + " \"sn\": \"31\",\n", + " \"sh\": null\n", " }\n", " ]\n", "}" @@ -829,27 +686,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 17, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jan/mambaforge/lib/python3.12/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n", - " \"cipher\": algorithms.TripleDES,\n", - "/home/jan/mambaforge/lib/python3.12/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n", - " \"class\": algorithms.TripleDES,\n" - ] - } - ], + "outputs": [], "source": [ "from python_workflow_definition.jobflow import load_workflow_json" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -858,175 +704,33 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m flow \u001b[38;5;241m=\u001b[39m \u001b[43mload_workflow_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworkflow_json_filename\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/repos/superstar54/python-workflow-definition/python_workflow_definition/src/python_workflow_definition/jobflow.py:187\u001b[0m, in \u001b[0;36mload_workflow_json\u001b[0;34m(file_name)\u001b[0m\n\u001b[1;32m 185\u001b[0m total_dict \u001b[38;5;241m=\u001b[39m _group_edges(edges_lst\u001b[38;5;241m=\u001b[39medges_new_lst)\n\u001b[1;32m 186\u001b[0m input_dict \u001b[38;5;241m=\u001b[39m _get_input_dict(nodes_dict\u001b[38;5;241m=\u001b[39mnodes_new_dict)\n\u001b[0;32m--> 187\u001b[0m new_total_dict \u001b[38;5;241m=\u001b[39m \u001b[43m_resort_total_lst\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtotal_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnodes_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnodes_new_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 188\u001b[0m task_lst \u001b[38;5;241m=\u001b[39m _get_workflow(\n\u001b[1;32m 189\u001b[0m nodes_dict\u001b[38;5;241m=\u001b[39mnodes_new_dict,\n\u001b[1;32m 190\u001b[0m input_dict\u001b[38;5;241m=\u001b[39minput_dict,\n\u001b[1;32m 191\u001b[0m total_dict\u001b[38;5;241m=\u001b[39mnew_total_dict,\n\u001b[1;32m 192\u001b[0m source_handles_dict\u001b[38;5;241m=\u001b[39msource_handles_dict,\n\u001b[1;32m 193\u001b[0m )\n\u001b[1;32m 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Flow(task_lst)\n", + "File \u001b[0;32m~/repos/superstar54/python-workflow-definition/python_workflow_definition/src/python_workflow_definition/jobflow.py:103\u001b[0m, in \u001b[0;36m_resort_total_lst\u001b[0;34m(total_dict, nodes_dict)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ind \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ordered_lst:\n\u001b[1;32m 102\u001b[0m source_lst \u001b[38;5;241m=\u001b[39m [sd[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msn\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m sd \u001b[38;5;129;01min\u001b[39;00m connect\u001b[38;5;241m.\u001b[39mvalues()]\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\u001b[43m[\u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mordered_lst\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mnodes_without_dep_lst\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msource_lst\u001b[49m\u001b[43m]\u001b[49m):\n\u001b[1;32m 104\u001b[0m ordered_lst\u001b[38;5;241m.\u001b[39mappend(ind)\n\u001b[1;32m 105\u001b[0m total_new_dict[ind] \u001b[38;5;241m=\u001b[39m connect\n", + "File \u001b[0;32m~/repos/superstar54/python-workflow-definition/python_workflow_definition/src/python_workflow_definition/jobflow.py:103\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ind \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ordered_lst:\n\u001b[1;32m 102\u001b[0m source_lst \u001b[38;5;241m=\u001b[39m [sd[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msn\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m sd \u001b[38;5;129;01min\u001b[39;00m connect\u001b[38;5;241m.\u001b[39mvalues()]\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m([s \u001b[38;5;129;01min\u001b[39;00m ordered_lst \u001b[38;5;129;01mor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m nodes_without_dep_lst \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m source_lst]):\n\u001b[1;32m 104\u001b[0m ordered_lst\u001b[38;5;241m.\u001b[39mappend(ind)\n\u001b[1;32m 105\u001b[0m total_new_dict[ind] \u001b[38;5;241m=\u001b[39m connect\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "flow = load_workflow_json(file_name=workflow_json_filename)" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-27 18:03:30,952 INFO Started executing jobs locally\n", - "2025-03-27 18:03:34,071 INFO Starting job - get_bulk_structure (9f8dfd72-24c3-4c94-a92a-9b7c6923cdf0)\n", - "2025-03-27 18:03:34,184 INFO Finished job - get_bulk_structure (9f8dfd72-24c3-4c94-a92a-9b7c6923cdf0)\n", - "2025-03-27 18:03:34,186 INFO Starting job - get_dict (42b70db9-a867-4960-9c9c-0245f9af45a3)\n", - "2025-03-27 18:03:34,192 INFO Finished job - get_dict (42b70db9-a867-4960-9c9c-0245f9af45a3)\n", - "2025-03-27 18:03:34,193 INFO Starting job - calculate_qe (a8ea4e49-953b-44ec-ab13-017b5e79bce1)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-27 18:04:04,205 INFO Finished job - calculate_qe (a8ea4e49-953b-44ec-ab13-017b5e79bce1)\n", - "2025-03-27 18:04:04,207 INFO Starting job - generate_structures 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"text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job get_dict_ab6337f6767889e55c56e5b06f78d353 was saved and received the ID: 20\n", - "The job calculate_qe_0c7e3dd81f83bb1c9fa294a63874b4b9 was saved and received the ID: 21\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job get_list_a6df7a07656486154523680b67dc543b was saved and received the ID: 22\n", - "The job get_list_f14c9adeb16b4b1b25e9c4c2e4b0cf13 was saved and received the ID: 23\n", - "The job plot_energy_volume_curve_5dea3fa7571ea91ed1aa1745897703c7 was saved and received the ID: 24\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "delayed_object.pull()" ] @@ -1941,7 +793,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "aiida", "language": "python", "name": "python3" }, @@ -1955,7 +807,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.5" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/python_workflow_definition/src/python_workflow_definition/aiida.py b/python_workflow_definition/src/python_workflow_definition/aiida.py index 3c240f3..16a643f 100644 --- a/python_workflow_definition/src/python_workflow_definition/aiida.py +++ b/python_workflow_definition/src/python_workflow_definition/aiida.py @@ -1,13 +1,10 @@ from importlib import import_module import traceback from aiida_workgraph import WorkGraph, task +from aiida_pythonjob.data.serializer import general_serializer import json from aiida import orm -@task.pythonjob() -def pickle_node(value): - """Handle data nodes""" - return value def load_workflow_json(file_name): @@ -21,90 +18,65 @@ def load_workflow_json(file_name): if isinstance(identifier, str) and "." in identifier: p, m = identifier.rsplit(".", 1) mod = import_module(p) - _func = getattr(mod, m) - func = task.pythonjob()(_func) - # I use the register_pickle_by_value, because the function is defined in a local file - wg.add_task(func, register_pickle_by_value=True) - + func = getattr(mod, m) + wg.add_task(func) # Remove the default result output, because we will add the outputs later from the data in the link del wg.tasks[-1].outputs["result"] + task_name_mapping[id] = wg.tasks[-1] else: # data task - wg.add_task(pickle_node, value=identifier) + data_node = general_serializer(identifier) + task_name_mapping[id] = data_node - task_name_mapping[id] = wg.tasks[-1].name # add links for link in data["edges"]: - if link["sh"] is None: - link["sh"] = "result" - try: - from_task = wg.tasks[task_name_mapping[str(link["sn"])]] - # because we are not define the outputs explicitly during the pythonjob creation - # we add it here, and assume the output exit - if link["sh"] not in from_task.outputs: - # if str(link["sh"]) not in from_task.outputs: - from_socket = from_task.add_output( - "workgraph.any", - name=link["sh"], - # name=str(link["sh"]), - metadata={"is_function_output": True}, - ) - else: - from_socket = from_task.outputs[link["sh"]] - to_task = wg.tasks[task_name_mapping[str(link["tn"])]] - # if the input is not exit, it means we pass the data into to the kwargs - # in this case, we add the input socket - if link["th"] not in to_task.inputs: - # - to_socket = to_task.add_input( - "workgraph.any", - name=link["th"], - metadata={"is_function_input": True}, - ) - else: - to_socket = to_task.inputs[link["th"]] - wg.add_link(from_socket, to_socket) - except Exception as e: - traceback.print_exc() - print("Failed to link", link, "with error:", e) + to_task = task_name_mapping[str(link["tn"])] + # if the input is not exit, it means we pass the data into to the kwargs + # in this case, we add the input socket + if link["th"] not in to_task.inputs: + to_socket = to_task.add_input( "workgraph.any", name=link["th"]) + else: + to_socket = to_task.inputs[link["th"]] + from_task = task_name_mapping[str(link["sn"])] + if isinstance(from_task, orm.Data): + to_socket.value = from_task + else: + try: + if link["sh"] is None: + link["sh"] = "result" + # because we are not define the outputs explicitly during the pythonjob creation + # we add it here, and assume the output exit + if link["sh"] not in from_task.outputs: + # if str(link["sh"]) not in from_task.outputs: + from_socket = from_task.add_output( + "workgraph.any", + name=link["sh"], + # name=str(link["sh"]), + metadata={"is_function_output": True}, + ) + else: + from_socket = from_task.outputs[link["sh"]] + + wg.add_link(from_socket, to_socket) + except Exception as e: + traceback.print_exc() + print("Failed to link", link, "with error:", e) return wg def write_workflow_json(wg, file_name): + from aiida_workgraph.socket import TaskSocketNamespace data = {"nodes": {}, "edges": []} node_name_mapping = {} + data_node_name_mapping = {} i = 0 for node in wg.tasks: executor = node.get_executor() node_name_mapping[node.name] = i callable_name = executor["callable_name"] - - if callable_name == "pickle_node": - input_value = data["nodes"][str(i)] = node.inputs.value.value - try: - if isinstance(input_value, orm.Data): - if isinstance(input_value, orm.List): - data["nodes"][str(i)] = input_value.get_list() - elif isinstance(input_value, orm.Dict): - data["nodes"][str(i)] = input_value.get_dict() - else: - data["nodes"][str(i)] = input_value.value - else: - data["nodes"][str(i)] = input_value - except: - import traceback - - traceback.print_stack() - raise - # raise - # import ipdb; ipdb.set_trace() - - else: - callable_name = f"{executor['module_path']}.{callable_name}" - - data["nodes"][str(i)] = callable_name - + callable_name = f"{executor['module_path']}.{callable_name}" + data["nodes"][str(i)] = callable_name i += 1 for link in wg.links: @@ -117,6 +89,32 @@ def write_workflow_json(wg, file_name): link_data["sn"] = node_name_mapping[link_data.pop("from_node")] link_data["sh"] = link_data.pop("from_socket") data["edges"].append(link_data) + + for node in wg.tasks: + for input in node.inputs: + # assume namespace is not used as input + if isinstance(input, TaskSocketNamespace): + continue + if isinstance(input.value, orm.Data): + if input.value.uuid not in data_node_name_mapping: + if isinstance(input.value, orm.List): + raw_value = input.value.get_list() + elif isinstance(input.value, orm.Dict): + raw_value = input.value.get_dict() + else: + raw_value = input.value.value + input_node_name = str(i) + data["nodes"][input_node_name] = raw_value + data_node_name_mapping[input.value.uuid] = input_node_name + i += 1 + else: + input_node_name = data_node_name_mapping[input.value.uuid] + data["edges"].append({ + "tn": node_name_mapping[node.name], + "th": input._name, + "sn": input_node_name, + "sh": None + }) with open(file_name, "w") as f: # json.dump({"nodes": data[], "edges": edges_new_lst}, f) From 6ac1f88e5b84b2bf415e9f8dcd79d3dbb799d90f Mon Sep 17 00:00:00 2001 From: superstar54 Date: Tue, 1 Apr 2025 09:45:06 +0200 Subject: [PATCH 2/7] use int for node name in the edges --- aiida_qe.ipynb | 1124 ++++++++++++++++- .../src/python_workflow_definition/aiida.py | 7 +- universal_workflow_qe.ipynb | 407 +----- 3 files changed, 1131 insertions(+), 407 deletions(-) diff --git a/aiida_qe.ipynb b/aiida_qe.ipynb index 54f0cca..621323a 100644 --- a/aiida_qe.ipynb +++ b/aiida_qe.ipynb @@ -230,7 +230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d15bd7f73ab14488b65fa5997453b4bd", + "model_id": "e2cf9fa0e97e4cd2b074dc33ba0f23a6", "version_major": 2, "version_minor": 1 }, @@ -294,8 +294,7 @@ " 3\n", " ],\n", " \"22\": {\n", - " \"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\",\n", - " \"node_type\": \"data.core.dict.Dict.\"\n", + " \"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"\n", " },\n", " \"23\": 0.02,\n", " \"24\": \"mini\",\n", @@ -467,205 +466,205 @@ " {\n", " \"tn\": 0,\n", " \"th\": \"element\",\n", - " \"sn\": \"17\",\n", + " \"sn\": 17,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 0,\n", " \"th\": \"a\",\n", - " \"sn\": \"18\",\n", + " \"sn\": 18,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 0,\n", " \"th\": \"cubic\",\n", - " \"sn\": \"19\",\n", + " \"sn\": 19,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 1,\n", " \"th\": \"calculation\",\n", - " \"sn\": \"20\",\n", + " \"sn\": 20,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 1,\n", " \"th\": \"kpts\",\n", - " \"sn\": \"21\",\n", + " \"sn\": 21,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 1,\n", " \"th\": \"pseudopotentials\",\n", - " \"sn\": \"22\",\n", + " \"sn\": 22,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 1,\n", " \"th\": \"smearing\",\n", - " \"sn\": \"23\",\n", + " \"sn\": 23,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 2,\n", " \"th\": \"working_directory\",\n", - " \"sn\": \"24\",\n", + " \"sn\": 24,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 3,\n", " \"th\": \"strain_lst\",\n", - " \"sn\": \"25\",\n", + " \"sn\": 25,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 6,\n", " \"th\": \"calculation\",\n", - " \"sn\": \"26\",\n", + " \"sn\": 26,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 6,\n", " \"th\": \"kpts\",\n", - " \"sn\": \"21\",\n", + " \"sn\": 21,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 6,\n", " \"th\": \"pseudopotentials\",\n", - " \"sn\": \"22\",\n", + " \"sn\": 22,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 6,\n", " \"th\": \"smearing\",\n", - " \"sn\": \"23\",\n", + " \"sn\": 23,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 7,\n", " \"th\": \"working_directory\",\n", - " \"sn\": \"27\",\n", + " \"sn\": 27,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 8,\n", " \"th\": \"calculation\",\n", - " \"sn\": \"26\",\n", + " \"sn\": 26,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 8,\n", " \"th\": \"kpts\",\n", - " \"sn\": \"21\",\n", + " \"sn\": 21,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 8,\n", " \"th\": \"pseudopotentials\",\n", - " \"sn\": \"22\",\n", + " \"sn\": 22,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 8,\n", " \"th\": \"smearing\",\n", - " \"sn\": \"23\",\n", + " \"sn\": 23,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 9,\n", " \"th\": \"working_directory\",\n", - " \"sn\": \"28\",\n", + " \"sn\": 28,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 10,\n", " \"th\": \"calculation\",\n", - " \"sn\": \"26\",\n", + " \"sn\": 26,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 10,\n", " \"th\": \"kpts\",\n", - " \"sn\": \"21\",\n", + " \"sn\": 21,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 10,\n", " \"th\": \"pseudopotentials\",\n", - " \"sn\": \"22\",\n", + " \"sn\": 22,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 10,\n", " \"th\": \"smearing\",\n", - " \"sn\": \"23\",\n", + " \"sn\": 23,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 11,\n", " \"th\": \"working_directory\",\n", - " \"sn\": \"29\",\n", + " \"sn\": 29,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 12,\n", " \"th\": \"calculation\",\n", - " \"sn\": \"26\",\n", + " \"sn\": 26,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 12,\n", " \"th\": \"kpts\",\n", - " \"sn\": \"21\",\n", + " \"sn\": 21,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 12,\n", " \"th\": \"pseudopotentials\",\n", - " \"sn\": \"22\",\n", + " \"sn\": 22,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 12,\n", " \"th\": \"smearing\",\n", - " \"sn\": \"23\",\n", + " \"sn\": 23,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 13,\n", " \"th\": \"working_directory\",\n", - " \"sn\": \"30\",\n", + " \"sn\": 30,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 14,\n", " \"th\": \"calculation\",\n", - " \"sn\": \"26\",\n", + " \"sn\": 26,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 14,\n", " \"th\": \"kpts\",\n", - " \"sn\": \"21\",\n", + " \"sn\": 21,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 14,\n", " \"th\": \"pseudopotentials\",\n", - " \"sn\": \"22\",\n", + " \"sn\": 22,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 14,\n", " \"th\": \"smearing\",\n", - " \"sn\": \"23\",\n", + " \"sn\": 23,\n", " \"sh\": null\n", " },\n", " {\n", " \"tn\": 15,\n", " \"th\": \"working_directory\",\n", - " \"sn\": \"31\",\n", + " \"sn\": 31,\n", " \"sh\": null\n", " }\n", " ]\n", @@ -706,31 +705,173 @@ "cell_type": "code", "execution_count": 19, "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m flow \u001b[38;5;241m=\u001b[39m \u001b[43mload_workflow_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworkflow_json_filename\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/repos/superstar54/python-workflow-definition/python_workflow_definition/src/python_workflow_definition/jobflow.py:187\u001b[0m, in \u001b[0;36mload_workflow_json\u001b[0;34m(file_name)\u001b[0m\n\u001b[1;32m 185\u001b[0m total_dict \u001b[38;5;241m=\u001b[39m _group_edges(edges_lst\u001b[38;5;241m=\u001b[39medges_new_lst)\n\u001b[1;32m 186\u001b[0m input_dict \u001b[38;5;241m=\u001b[39m _get_input_dict(nodes_dict\u001b[38;5;241m=\u001b[39mnodes_new_dict)\n\u001b[0;32m--> 187\u001b[0m new_total_dict \u001b[38;5;241m=\u001b[39m \u001b[43m_resort_total_lst\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtotal_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnodes_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnodes_new_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 188\u001b[0m task_lst \u001b[38;5;241m=\u001b[39m _get_workflow(\n\u001b[1;32m 189\u001b[0m nodes_dict\u001b[38;5;241m=\u001b[39mnodes_new_dict,\n\u001b[1;32m 190\u001b[0m input_dict\u001b[38;5;241m=\u001b[39minput_dict,\n\u001b[1;32m 191\u001b[0m total_dict\u001b[38;5;241m=\u001b[39mnew_total_dict,\n\u001b[1;32m 192\u001b[0m source_handles_dict\u001b[38;5;241m=\u001b[39msource_handles_dict,\n\u001b[1;32m 193\u001b[0m )\n\u001b[1;32m 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Flow(task_lst)\n", - "File \u001b[0;32m~/repos/superstar54/python-workflow-definition/python_workflow_definition/src/python_workflow_definition/jobflow.py:103\u001b[0m, in \u001b[0;36m_resort_total_lst\u001b[0;34m(total_dict, nodes_dict)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ind \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ordered_lst:\n\u001b[1;32m 102\u001b[0m source_lst \u001b[38;5;241m=\u001b[39m [sd[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msn\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m sd \u001b[38;5;129;01min\u001b[39;00m connect\u001b[38;5;241m.\u001b[39mvalues()]\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\u001b[43m[\u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mordered_lst\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mnodes_without_dep_lst\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msource_lst\u001b[49m\u001b[43m]\u001b[49m):\n\u001b[1;32m 104\u001b[0m ordered_lst\u001b[38;5;241m.\u001b[39mappend(ind)\n\u001b[1;32m 105\u001b[0m total_new_dict[ind] \u001b[38;5;241m=\u001b[39m connect\n", - "File \u001b[0;32m~/repos/superstar54/python-workflow-definition/python_workflow_definition/src/python_workflow_definition/jobflow.py:103\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ind \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ordered_lst:\n\u001b[1;32m 102\u001b[0m source_lst \u001b[38;5;241m=\u001b[39m [sd[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msn\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m sd \u001b[38;5;129;01min\u001b[39;00m connect\u001b[38;5;241m.\u001b[39mvalues()]\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m([s \u001b[38;5;129;01min\u001b[39;00m ordered_lst \u001b[38;5;129;01mor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m nodes_without_dep_lst \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m source_lst]):\n\u001b[1;32m 104\u001b[0m ordered_lst\u001b[38;5;241m.\u001b[39mappend(ind)\n\u001b[1;32m 105\u001b[0m total_new_dict[ind] \u001b[38;5;241m=\u001b[39m connect\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "flow = load_workflow_json(file_name=workflow_json_filename)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-04-01 09:38:07,741 INFO Started executing jobs locally\n", + "2025-04-01 09:38:08,025 INFO Starting job - get_bulk_structure (4e19cf85-8556-41e9-8a66-7970c772779d)\n", + "2025-04-01 09:38:08,034 INFO Finished job - get_bulk_structure (4e19cf85-8556-41e9-8a66-7970c772779d)\n", + "2025-04-01 09:38:08,038 INFO Starting job - get_dict (332fbe1b-bfda-495a-9184-37aed1353865)\n", + "2025-04-01 09:38:08,046 INFO Finished job - get_dict (332fbe1b-bfda-495a-9184-37aed1353865)\n", + "2025-04-01 09:38:08,049 INFO Starting job - calculate_qe (9bf70cee-1283-468a-b4f4-fcc9f16129f7)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:375970] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-04-01 09:39:22,406 INFO Finished job - calculate_qe (9bf70cee-1283-468a-b4f4-fcc9f16129f7)\n", + "2025-04-01 09:39:22,408 INFO Starting job - generate_structures 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(15907897-f7e6-4639-9876-42ed4dadaae1)\n", + "2025-04-01 09:39:22,490 INFO Finished job - get_dict (15907897-f7e6-4639-9876-42ed4dadaae1)\n", + "2025-04-01 09:39:22,492 INFO Starting job - calculate_qe (a295270d-87db-41cc-aac2-88f7bd0b94d0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:377581] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-04-01 09:39:32,629 INFO Finished job - calculate_qe (a295270d-87db-41cc-aac2-88f7bd0b94d0)\n", + "2025-04-01 09:39:32,631 INFO Starting job - calculate_qe (9b7f6e91-aa74-48f0-8364-e5d344d8a274)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:377850] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-04-01 09:39:43,130 INFO Finished job - calculate_qe (9b7f6e91-aa74-48f0-8364-e5d344d8a274)\n", + "2025-04-01 09:39:43,133 INFO Starting job - calculate_qe (3427c80c-adb3-4cb5-a311-6a88af0d9aa7)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:378050] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-04-01 09:39:59,685 INFO Finished job - calculate_qe (3427c80c-adb3-4cb5-a311-6a88af0d9aa7)\n", + "2025-04-01 09:39:59,686 INFO Starting job - calculate_qe (a77c9b2d-6c97-443e-8484-aa140bf4f275)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:378429] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-04-01 09:40:19,201 INFO Finished job - calculate_qe (a77c9b2d-6c97-443e-8484-aa140bf4f275)\n", + "2025-04-01 09:40:19,203 INFO Starting job - calculate_qe (b8e75b04-9033-483f-a53f-3a77c8540186)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:378779] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-04-01 09:40:40,374 INFO Finished job - calculate_qe (b8e75b04-9033-483f-a53f-3a77c8540186)\n", + "2025-04-01 09:40:40,376 INFO Starting job - get_list (20193789-53c5-4a9d-95be-b4470eb3ac0f)\n", + "2025-04-01 09:40:40,392 INFO Finished job - get_list (20193789-53c5-4a9d-95be-b4470eb3ac0f)\n", + "2025-04-01 09:40:40,395 INFO Starting job - get_list (b389bc34-c79a-47ca-bf29-78aeab4fc8c4)\n", + "2025-04-01 09:40:40,408 INFO Finished job - get_list (b389bc34-c79a-47ca-bf29-78aeab4fc8c4)\n", + "2025-04-01 09:40:40,410 INFO Starting job - plot_energy_volume_curve (2a374153-788c-47c5-946e-e382992e4b38)\n", + "2025-04-01 09:40:40,745 INFO Finished job - plot_energy_volume_curve (2a374153-788c-47c5-946e-e382992e4b38)\n", + "2025-04-01 09:40:40,748 INFO Finished executing jobs locally\n" + ] + }, + { + "data": { + "text/plain": [ + "{'4e19cf85-8556-41e9-8a66-7970c772779d': {1: Response(output='{\"immutable_id\": null, \"last_modified\": null, \"elements\": [\"Al\"], \"nelements\": 1, \"elements_ratios\": [1.0], \"chemical_formula_descriptive\": \"Al4\", \"chemical_formula_reduced\": \"Al\", \"chemical_formula_hill\": null, \"chemical_formula_anonymous\": \"A\", \"dimension_types\": [1, 1, 1], \"nperiodic_dimensions\": 3, \"lattice_vectors\": [[4.05, 0.0, 0.0], [0.0, 4.05, 0.0], [0.0, 0.0, 4.05]], 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stop_jobflow=False, job_dir=PosixPath('/home/xing/repos/superstar54/python-workflow-definition'))}}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"delayed_object.draw()" @@ -783,12 +1664,131 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job get_bulk_structure_f1e730ed97e30e5439e855d2ac41396f was saved and received the ID: 1\n", + "The job get_dict_8c1e72002054a7529280a3e11ba005b5 was saved and received the ID: 2\n", + "The job calculate_qe_5e2d55d40f947e1c6791f89f7d93cd7c was saved and received the ID: 3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:379331] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job generate_structures_6042e2f1a150e0a8dc69909c3357e0e7 was saved and received the ID: 4\n", + "The job get_dict_773687310289049123463477639b2e74 was saved and received the ID: 5\n", + "The job calculate_qe_4d5f495146ebc0ce797711eb10c9a42c was saved and received the ID: 6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:381043] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job get_dict_29ff39cf860e1a7f4c8cc7d874b08a8d was saved and received the ID: 7\n", + "The job calculate_qe_cd318257eb4a8785b66151e9a85c382c was saved and received the ID: 8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:381257] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job get_dict_e3270a64b9a4153a91d4a851e538ad44 was saved and received the ID: 9\n", + "The job calculate_qe_b4bb4178ec80b7b49d05982d47a707f4 was saved and received the ID: 10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:381487] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job get_dict_b526e8a023fce1c4a4b8588b668f16e8 was saved and received the ID: 11\n", + "The job calculate_qe_71e06766387aec54f502114cdbbb1e85 was saved and received the ID: 12\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:381880] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job get_dict_8b465dbd4ff6930762a35eac7d987f8e was saved and received the ID: 13\n", + "The job calculate_qe_716b050690854b48cae2bfb572cc5eb6 was saved and received the ID: 14\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[thinkpad:382337] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job get_list_a28a868564f34fcd434fb31fd2b74340 was saved and received the ID: 15\n", + "The job get_list_d39a55ccd20cacf3dbeb1cfe70d08ed4 was saved and received the ID: 16\n", + "The job plot_energy_volume_curve_6f4c92d25a5f876513d595238396fafa was saved and received the ID: 17\n" + ] + }, + { + "data": { + "image/png": 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69+/PlClTWLlyJfv372f27Nl88sknjBkzBiie2psyZQpvvvkm8+bNY8+ePUybNo3du3dz9913m/Je7ankfnE/bE3heFaeuWFERERqKVNHli7F559/zoMPPsjgwYMBGDVqFG+//XaZbRISEsjIyCj9+quvvmLq1KncfvvtnDp1itDQUJ5//nnuv//+0m0efvhhcnNzmTx5MqdOnaJLly4sXbqU1q3r/iX13UIa0zXYh7hDp/li3UEeGtjW7EgiIiK1jkOss1TbOdI6S3/0XdwRHvoqjmaN3Fn9xDW4uTjEYKOIiMhlq1PrLEnNua5Tc/y93Tmelcei7UfNjiMiIlLrqCzVc67OTozvFQrArNXJaKBRRESkLJUl4bZeIbi5OLHtcAabD542O46IiEitorIkNG3ozvVdAgGYtVqLVIqIiPyeypIAMOHc/eIW70glJeOsuWFERERqEZUlAaBDoJWeLZtQZDP4dO0Bs+OIiIjUGipLUuquc6NLX64/SG5BkblhREREagmVJSk1KDKAFj4NSM8p4Lu4I2bHERERqRVUlqSUs5OFO/toGQEREZHfU1mSMm7uEUIDV2d2p2axdt9Js+OIiIiYTmVJyrB6unJD9xYAzF6dbG4YERGRWkBlSc4z8dyJ3kvj0zh0KsfcMCIiIiZTWZLztPFrxJ/a+mIYMGdNstlxRERETKWyJOUqGV36euMhsvMKzQ0jIiJiIpUlKdeAdn609PUiK7eQbzYfNjuOiIiIaVSWpFxOThbujDq3jMCaZGw2LSMgIiL1k8qSXNBNPYJp6O7CvuPZ/Jp03Ow4IiIiplBZkgtq6O7C2B5BQPEilSIiIvWRypJUaEKfMCwW+CXxOHuPnzE7joiIiN2pLEmFQpt6cW24H6BlBEREpH5SWZKLmti3JQDzNh0m42yByWlERETsS2VJLqpP66a0829ITn4RczceMjuOiIiIXaksyUVZLBYm9CkeXZqzNpkiLSMgIiL1iMqSVMqYbi3w8XTl0Kmz/ByfZnYcERERu1FZkkpp4ObMLVeGAFpGQERE6heVJam06KhQnJ0srN13kviUTLPjiIiI2IXKklRaC58GDOngD2gZARERqT9UluSSlCwjsGDLEU5l55ucRkREpOapLMkl6RHamI4tvMkrtPHl+oNmxxEREalxKktySX6/jMCnaw9QUGQzOZGIiEjNUlmSSzayS3N8G7qRmpnLkp2pZscRERGpUSpLcsncXZy5rVcooGUERESk7lNZkioZ3ysEV2cLmw6ks+3wabPjiIiI1BiVJakSP28PhndqDmh0SURE6jaVJamykmUEfth2lGNZuSanERERqRkqS1JlXYJ96B7iQ0GRweexWkZARETqJpUluSwlo0ufrztAXmGRyWlERESqn8qSXJahHQMI8PbgxJl8ftiaYnYcERGRaqeyJJfF1dmJ6KjiZQRmr0nGMAyTE4mIiFQvlSW5bLf2DMHdxYntRzLYdCDd7DgiIiLVSmVJLlsTLzdGd20BaBkBERGpe1SWpFpM6BsGQMzOVI6ePmtuGBERkWqksiTVIqK5N71bNaHIZvBp7AGz44iIiFQblSWpNiXLCHy5/iBn87WMgIiIXL6DJ3P4aNV+imzmXUCksiTVZmCEP0GNG3A6p4Bv446YHUdERByczWYwZd5Wnv1hFy/8GG9aDpUlqTbOThbujAoDYNbq/VpGQERELsunsQdYt/8Unm7OpX9fzKCyJNVq3JXBeLo5k5h2hrV7T5odR0REHNSBk9nMXLwbgL8PCyekqadpWVSWpFpZG7hyY/cgAD7WMgIiIlIFNpvBlLnbOFtQRFSrpozvFWpqHpUlqXZ39gkD4OfdaRw4mW1uGBERcTiz1ySzPrl4+u2lmzrj5GQxNY/KklS7Nn4NuapdMwwD5qzRMgIiIlJ5+09k89KS4um3J6+LILiJedNvJVSWpEZMPLdI5dyNhziTV2huGBERcQhFNoMpc7eSW2Cjb5um3N4rxOxIgMqS1JD+bZvRyteLrLxC5m86bHYcERFxALNW72fjgXS83Jx58cbOWCzmTr+VUFmSGuHkZCm9BcrsNcnYTFxMTEREar99x8/w8pIEAP4xPJKgxuZPv5VQWZIac0P3IBq5u7D/RDa/JB43O46IiNRSRTaDx+ZuJa/Qxp/a+nJrz2CzI5WhsiQ1pqG7C+OuLP6Fn7Um2dwwIiJSa328aj+bD56mobsLM2vR9FsJlSWpUXdGhWGxwK+Jx9lz7IzZcUREpJbZc+wMr/xUPP321PAIWvg0MDnR+VSWpEaFNPVkYIQ/ALPX7Dc5jYiI1CZF5+79lldo46p2zbj5yto1/VZCZUlq3MRzi1TO33SEjLMF5oYREZFa47+/7WPLwdM0cndh5g2dat30WwmVJalxUa2b0t6/EWcLivjfhkNmxxERkVpgz7EsXl2aCMC0EZEE1sLptxIqS1LjLBZL6SKVc9YmU6RlBERE6rXCIhuPzt1GfqGNAe2bMbZHkNmRKqSyJHYxulsLfDxdOZx+lqW70syOIyIiJvrwt/1sPXSaRh4uzKjF028lVJbELjxcnbm1Z/Gy9TrRW0Sk/kpMy+L1c9NvT4+IpLm19k6/lVBZEruJ7h2Ks5OF2H2niE/JNDuOiIjYWWGRjcfmbiW/yMY14X7cdEXtnn4r4TBlKT09nejoaKxWK1arlejoaE6fPl3hPmfOnGHSpEkEBQXRoEEDIiIieO+998psk5qaSnR0NAEBAXh5edG9e3fmzZtXg++k/gr0acDQjgFA8f1/RESkfvng131sO5yBt4NMv5VwmLJ02223ERcXR0xMDDExMcTFxREdHV3hPpMnTyYmJobPPvuM+Ph4Jk+ezAMPPMB3331Xuk10dDQJCQksXLiQ7du3c8MNN3DzzTezZcuWmn5L9dJd5070/jbuKCfP5JkbRkRE7CYhNYs3lhVPvz0zsgP+3h4mJ6o8hyhL8fHxxMTE8N///peoqCiioqL48MMP+eGHH0hISLjgfmvXruXOO+9kwIABhIWF8ec//5kuXbqwcePGMts88MAD9OzZk1atWvHUU0/h4+PD5s2b7fHW6p3uIY3p1MJKfqGNr7SMgIhIvVBwbvqtoMhgYIQfN3RvYXakS+IQZWnt2rVYrVZ69epV+lzv3r2xWq2sWbPmgvv169ePhQsXcuTIEQzDYMWKFSQmJjJkyJAy23z99decOnUKm83GV199RV5eHgMGDKjJt1Rv/X4ZgU/XHqCgyGZuIBERqXHvr9zL9iMZWBu48sIYx5l+K+EQZSk1NRU/P7/znvfz8yM1NfWC+7355ptERkYSFBSEm5sbQ4cO5d1336Vfv36l23z99dcUFhbStGlT3N3due+++1iwYAGtW7e+4Ovm5eWRmZlZ5iGVN7xzc3wbupOamcviHRf+309ERBxffEomby5PAmD6qEj8HGj6rYSpZWn69OlYLJYKHyVTZuW1UMMwKmynb775JrGxsSxcuJBNmzbx6quv8te//pVly5aVbvPUU0+Rnp7OsmXL2LhxI4888ghjx45l+/btF3zdGTNmlJ5obrVaCQ6unfeyqa3cXZwZ37t4GQGd6C0iUnf9fvptUKQ/o7s61vRbCYthGKYtp3zixAlOnDhR4TZhYWF88cUXPPLII+dd/ebj48Prr7/OxIkTz9vv7NmzWK1WFixYwPDhw0ufv+eeezh8+DAxMTHs3buXNm3asGPHDjp06FC6zcCBA2nTpg3vv/9+uZny8vLIy/v/k5MzMzMJDg4mIyMDb2/vyrz1eu9YVi59Zy6noMjg27/1pWuwj9mRRESkmv17WRKvL0vEx9OVnyZfhV+j2jWqlJmZidVqvejfbxc7ZjqPr68vvr6+F90uKiqKjIwM1q9fT8+ePQFYt24dGRkZ9OnTp9x9CgoKKCgowMmp7OCZs7MzNlvxeTI5OTkAFW5THnd3d9zd3S+aWy7Mr5EHIzsH8s2WI8xevZ83bulmdiQREalGu45m8ta56bd/jupQ64rSpXCIc5YiIiIYOnQo9957L7GxscTGxnLvvfcyYsQI2rdvX7pdeHg4CxYsAMDb25v+/fszZcoUVq5cyf79+5k9ezaffPIJY8aMKd2+TZs23Hfffaxfv569e/fy6quvsnTpUkaPHm3GW61XJvZtCcCi7Skcy8w1OY2IiFSX/MLi6bdCm8GQDv6M6hJodqTL4hBlCeDzzz+nU6dODB48mMGDB9O5c2c+/fTTMtskJCSQkZFR+vVXX33FlVdeye23305kZCQzZ87k+eef5/777wfA1dWVH3/8kWbNmjFy5Eg6d+7MJ598wpw5c7juuuvs+v7qo05BVnqENqagyOCz2ANmxxERkWryzoo97ErJpLGnK8+Ndryr3/7I1HOW6orKznnK+X7YdpRJX2yhqZcba6Zeg7uLs9mRRETkMuw4ksHod1ZTaDN469ZujKzFo0qV/fvtMCNLUjcN6RBAc6sHJ7Pz+X5ritlxRETkMvx++m1YxwBGdG5udqRqobIkpnJ1diI6KhQoXkZAA50iIo7r7eVJ7E7NoomXG8+O7ujw028lVJbEdLdeGYK7ixM7j2ayITnd7DgiIlIFO45k8M7KvQA8e31HfBvWnavGVZbEdI293BjTrXihstlrtEiliIijySss4tH/baXIZjC8U3OG15HptxIqS1IrTDh3v7glO9M4cvqsuWFEROSSvPXzHhLSsmjq5ca/ru9w8R0cjMqS1ArhAd70ad2UIpvBJ2uTzY4jIiKVtO3wad77pXj67bnRHWlah6bfSqgsSa1RskjlV+sPkZNfaHIaERG5mLzCIh6bWzz9NrJLIMM61a3ptxIqS1JrXBPuR3CTBmScLeDbLUfNjiMiIhfx72VJJKadwbehG/8cVfem30qoLEmt4exk4c6oMKD4RG8tIyAiUnvFHTrN+6XTb51o4uVmcqKao7Iktcq4K4PxcnMmMe0Mq/ecNDuOiIiUI7egePrNZsD1XQMZ2jHA7Eg1SmVJahVvD1duuiIIKF6kUkREap83liWx59gZfBu6M31k3Z1+K6GyJLXOHX3CAFiecIzkE9nmhhERkTI2H0znP78WT7+9MKYjjevw9FsJlSWpdVo3a8iA9s0wDJijZQRERGqN3IIippybfhvTrQWDO9Tt6bcSVSpL2dn6177UrJJlBOZuPExWboHJaUREBOC1pYnsPZ5Ns0buPDMy0uw4dlOlsuTv789dd93FqlWrqjuPCABXtfWldTMvzuQVMm/TYbPjiIjUe5sOpPPhb/sAmDGmEz6edX/6rUSVytKXX35JRkYG1157Le3atWPmzJkcPap1caT6WCwWJpw7d2nOmmRsNi0jICJilpLpN8OAG7q3YGCkv9mR7KpKZWnkyJHMnz+fo0eP8pe//IUvv/yS0NBQRowYwTfffENhoVZflst3Q/cgGnm4kHwyh5WJx8yOIyJSb72yJIF9J7Lx93bnmRF1/+q3P7qsE7ybNm3K5MmT2bp1K6+99hrLli3jpptuIjAwkKeffpqcnJzqyin1kJe7C7dcGQzArNXJ5oYREamnNiaf4qNzS7nMuKETVk9XkxPZ32WVpdTUVF566SUiIiL4+9//zk033cTPP//M66+/zoIFCxg9enQ1xZT66o6oMJws8FvSCZLSssyOIyJSr5zNL1580jDgpiuCuCa8fk2/lXCpyk7ffPMNs2bNYsmSJURGRvK3v/2N8ePH4+PjU7pN165d6datW3XllHoquIknAyP8+WlXGrPXJPP8mE5mRxIRqTdeXpJA8skcArw9mDai/lz99kdVGlmaOHEigYGBrF69mri4OCZNmlSmKAG0atWKf/zjH9WRUeq5kmUEvtl8hIwcLSMgImIP6/efYtaac9NvN3bC2qD+Tb+VqNLIUkpKCp6enhVu06BBA5555pkqhRL5vd6tmhAe0IjdqVl8teEg9/VvbXYkEZE6LSe/kCnziqffxvUI4ur2fmZHMlWVRpYKCwvJzMw875GVlUV+fn51Z5R6zmKxcNe50aVP1h6gsMhmciIRkbrtpZgEDpzMobnVg6fq8fRbiSqVJR8fHxo3bnzew8fHhwYNGhAaGsozzzyDzaY/alI9RnUNpLGnK0dOn2VZfJrZcURE6qzYfSeZvSYZgJk3dsbbo/5Ov5WoUlmaPXs2gYGBPPnkk3z77bcsWLCAJ598khYtWvDee+/x5z//mTfffJOZM2dWd16ppzxcnbmtVwgAH2sZARGRGpGTX8jj87YBcGvPYPq3a2ZyotqhSucszZkzh1dffZVx48aVPjdq1Cg6derEBx98wM8//0xISAjPP/88Tz75ZLWFlfotuncYH/yyj/X7T7HzaAYdAq1mRxIRqVNeXLybg6dyaOHTgCevizA7Tq1RpZGltWvXlrssQLdu3Vi7di0A/fr14+DBg5eXTuR3AqweDOvUHNAilSIi1W3N3hPMWXsAgJk3dqKRpt9KVaksBQUF8dFHH533/EcffURwcPGKyydPnqRx48aXl07kD0ruF7cw7ignzuSZG0ZEpI7Izvv/6bfbeoXwp7aafvu9Kk3DvfLKK4wdO5bFixdz5ZVXYrFY2LBhA7t372bevHkAbNiwgZtvvrlaw4p0D/GhS5CVrYcz+HLdQR64tq3ZkUREHN7Mxbs5nH5W028XYDEMo0q3cz9w4ADvv/8+CQkJGIZBeHg49913H2FhYdUcsfbLzMzEarWSkZGBt7e32XHqvG+3HOHhr+Pwa+TOqieuwc3lsu7aIyJSr63Zc4Lb/rsOgM/v6UXfNr4mJ7Kfyv79vuSRpYKCAgYPHswHH3zAjBkzLiukSFVc16k5z/8Yz7GsPBbvSOH6ri3MjiQi4pDO5BUy5dz02/jeIfWqKF2KS/4nuaurKzt27MBisdREHpGLcnNxYnyvUEAneouIXI4XfoznyOmzBDVuwNRhmn67kCrNX9xxxx3lnuAtYi+39QrBzdmJuEOn2XIw3ew4IiIOZ1XSCb5YV3zV+ks3dcbLvUqnMdcLVToy+fn5/Pe//2Xp0qX06NEDLy+vMt9/7bXXqiWcyIU0a+TOyC6BzN98mFmrk+kWoisvRUQqKyu3gCfmF0+/3REVSp/Wmn6rSJXK0o4dO+jevTsAiYmJZb6n6Tmxl4l9w5i/+TA/bk/hyesiCLB6mB1JRMQhlEy/BTdpwBNDw82OU+tVqSytWLGiunOIXLKOLaxcGdaYDcnpfL7uAI8Obm92JBGRWu/XxON8uf4QAC/f1EXTb5VwWddc79mzhyVLlnD27FkAqrgKgUiVTezbEoAv1h0kt6DI5DQiIrVb5u+m3yb0CaN3q6YmJ3IMVSpLJ0+e5Nprr6Vdu3Zcd911pKSkAHDPPffw6KOPVmtAkYoMjvSnhU8DTmbns3DrUbPjiIjUas//EE9KRi6hTT15fKhG4yurSmVp8uTJuLq6cvDgQTw9PUufv/nmm4mJiam2cCIX4+LsRHTU/y8joNFNEZHyrUw4xtcbD2GxFE+/ebpp+q2yqlSWfvrpJ1588UWCgoLKPN+2bVsOHDhQLcFEKuuWK4PxcHUiPiWT9ftPmR1HRKTWyThbwN/nbweKp996tmxiciLHUqWylJ2dXWZEqcSJEydwd3e/7FAil8LH040x3YqLuxapFBE533M/7CI1M5ewpp48PkRXv12qKpWlq666ik8++aT0a4vFgs1m4+WXX+bqq6+utnAilTWxbxgAP+1K5dCpHHPDiIjUIit2H2PupsNYLPDK2C40cHM2O5LDqdKE5csvv8yAAQPYuHEj+fn5PP744+zcuZNTp06xevXq6s4oclHt/BvRr40vq/ac4NPYA7prtogIkJFTwN+/Kb767a6+LekRpum3qqjSyFJkZCTbtm2jZ8+eDBo0iOzsbG644Qa2bNlC69atqzujSKVM6BMGwFfrD5KTX2huGBGRWuBfP+wiLTOPVr5ePKa16KqsyqfCBwQE8M9//rM6s4hclmvC/Qht6smBkzl8s/kI43uHmh1JRMQ0y3alMX9z8fTby2M7a/rtMlS5LJ0+fZr169dz7NgxbDZbme/dcccdlx1M5FI5OVm4MyqMf/2wi9lrkrm9V4huvyMi9VJGTgFPLii++u3eP7XiilBNv12OKpWl77//nttvv53s7GwaNWpU5g+SxWJRWRLTjO0RxKs/JbDn2Bl+SzrBVe2amR1JRMTu/vn9To5l5dGqmRePDGpndhyHV6Vzlh599FHuuususrKyOH36NOnp6aWPU6e0zo2Yp5GHK2N7BAMwe02yuWFEREywdFca32w5gtO5q988XDX9drmqVJaOHDnCgw8+WO5aSyJmu7NPGBYLLN99jP0nss2OIyJiN+nZ+f8//XZVK7qHNDY5Ud1QpbI0ZMgQNm7cWN1ZRKpFS18vrm7vB8AcjS6JSD0y/fudHM/Ko41fQyYP1PRbdanSOUvDhw9nypQp7Nq1i06dOuHq6lrm+6NGjaqWcCJVNbFvGMt3H2PuxkM8Mrgd3h6uF99JRMSBLdmZyndxRzX9VgMsRhXuPOrkdOEBKYvFQlFR0WWFcjSZmZlYrVYyMjLw9vY2O44AhmEw6PVf2XPsDE+PiOSufi3NjiQiUmNOZecz+PVfOHEmn78MaM0TQ3VLk8qo7N/vKk3D2Wy2Cz7qW1GS2slisZQuUjlnbTJFtkv+N4GIiMN4ZuFOTpzJp61fQx4e2NbsOHXOJZWl6667joyMjNKvn3/+eU6fPl369cmTJ4mMjKy2cCKX44buLfD2cOHAyRxW7D5mdhwRkRoRsyOF77cexdnJwitju+Duoum36nZJZWnJkiXk5eWVfv3iiy+WWSqgsLCQhISE6ksnchk83Vy4tWcIALPW7Dc5jYhI9Tt5Jo9/LNgBwP39W9El2MfcQHXUJZWlP57eVIXTnUTsKjoqFCcLrN5zksS0LLPjiIhUq6cX7uRkdj7t/Rvx4LWafqspVTpnScRRBDX2ZHBkAACzViebG0ZEpBot2pbCom0pmn6zg0sqSxaL5bx7beneW1LbTewbBsCCLYc5nZNvbhgRkWpw4kwe074rnn7764DWdAqympyobrukdZYMw2DChAm4u7sDkJuby/3334+XlxdAmfOZRGqLni2bENncm10pmXy5/hB/GdDa7EgiIpfl6e92cCo7n/CARjxwjabfatoljSzdeeed+Pn5YbVasVqtjB8/nsDAwNKv/fz8dBNdqXUsFgsTzo0ufbo2mcIim7mBREQuww/bjvLj9lRczk2/ubnojJqadkkjS7NmzaqpHCI1alSXQF5cvJujGbn8tCuN6zo1NzuSiMglO56Vx7Rvz02/Xd2Gji00/WYPqqNSL3i4OnNbr3PLCKzWMgIi4ngMw+Cpb7eTnlNARHNvJl3dxuxI9YbKktQb43uH4uJkYUNyOjuOZFx8BxGRWmTh1qMs2Zl2bvqts6bf7MhhjnR6ejrR0dGl50dFR0eXWT28PGlpaUyYMIHAwEA8PT0ZOnQoSUlJZbbJy8vjgQcewNfXFy8vL0aNGsXhw4dr8J2IWfy9PUqn37SMgIg4kmNZuTyzcCcAD1zTlg6Bmn6zJ4cpS7fddhtxcXHExMQQExNDXFwc0dHRF9zeMAxGjx7Nvn37+O6779iyZQuhoaEMHDiQ7Ozs0u0efvhhFixYwFdffcWqVas4c+YMI0aM0D3u6qiSZQS+33qU41m6elNEaj/DMPjHgh2czimgQ6A3f71aV/Tam8VwgGW44+PjiYyMJDY2ll69egEQGxtLVFQUu3fvpn379uftk5iYSPv27dmxYwcdOnQAoKioCD8/P1588UXuueceMjIyaNasGZ9++ik333wzAEePHiU4OJgff/yRIUOGVCpfZe9aLLXD6HdWE3foNJMHtuMh3XBSRGq5b7cc4eGv43B1trBwUj8imuvvTHWp7N9vhxhZWrt2LVartbQoAfTu3Rur1cqaNWvK3adkzScPD4/S55ydnXFzc2PVqlUAbNq0iYKCAgYPHly6TWBgIB07drzg64rjKxld+mzdAfILtYyAiNRexzL/f/rtwWvaqiiZxCHKUmpqKn5+fuc97+fnR2pqarn7hIeHExoaytSpU0lPTyc/P5+ZM2eSmppKSkpK6eu6ubnRuHHjMvv6+/tf8HWhuIhlZmaWeYjjuK5Tc/y93TmelceP21PMjiMiUi7DMHhywXYyzhbQsYU392tBXdOYWpamT59eeguVCz02btwIlH9bFcMwLni7FVdXV+bPn09iYiJNmjTB09OTlStXMmzYMJydK75/TkWvCzBjxozSE82tVivBwcGX8K7FbK7OTozvFQoULyPgADPRIlIPLdhyhGXxx3B1tvDq2K64OjvE+EaddEmLUla3SZMmccstt1S4TVhYGNu2bSMtLe287x0/fhx/f/8L7nvFFVcQFxdHRkYG+fn5NGvWjF69etGjRw8AAgICyM/PJz09vczo0rFjx+jTp88FX3fq1Kk88sgjpV9nZmaqMDmY23qF8NaKPWw9nMHmg6e5IrTxxXcSEbGTtMxcpp+bfnt4YDvaBzQyOVH9ZmpZ8vX1xdfX96LbRUVFkZGRwfr16+nZsycA69atIyMjo8JSU8JqLb7EMikpiY0bN/Lss88CxWXK1dWVpUuXMm7cOABSUlLYsWMHL7300gVfz93dvfT+eOKYmjZ05/ougczddJhZq/erLIlIrWEYBlO/2U5mbiGdg6zcd1UrsyPVew4xphcREcHQoUO59957iY2NJTY2lnvvvZcRI0aUuRIuPDycBQsWlH49d+5cVq5cWbp8wKBBgxg9enTpCd1Wq5W7776bRx99lJ9//pktW7Ywfvx4OnXqxMCBA+3+PsW+Su4Xt3hHKikZZ80NIyJyzvzNR1i++xhuzk68MrYLLpp+M53D/C/w+eef06lTJwYPHszgwYPp3Lkzn376aZltEhISyMj4/5WZU1JSiI6OJjw8nAcffJDo6Gi+/PLLMvu8/vrrjB49mnHjxtG3b188PT35/vvvL3pekzi+DoFWerZsQpHN4LPYA2bHEREhNSOXf35/bvptUFva+Wv6rTZwiHWWajuts+S4YnakcP9nm2ns6craqdfi4aqSLCLmMAyDibM3sDLhOF2CfZh/f5RGlWpYnVpnSaSmDIoMoIVPA9JzCvgu7ojZcUSkHpu76TArE47j5uLEKzd1VlGqRfS/hNRrzk4W7uxTsoxAspYREBFTHD19lme/3wXAI4Pa0VbTb7WKypLUezf3CKGBqzO7U7OI3XfK7DgiUs8YhsHfv9lOVl4h3UJ8uPdPuvqttlFZknrP6unKDd1bAMWLVIqI2NP/Nh7i18Ti6beXb+qCs9OFF0UWc6gsifD/94tbGp/GoVM55oYRkXrjyOmzPPtDPACPDW5HG7+GJieS8qgsiQBt/Brxp7a+GAZ8sjbZ7DgiUg8YhsHf52/jTF4h3UN8uLufpt9qK5UlkXNKRpe+2nCI7LxCc8OISJ335fpD/JZ0AneX4sUnNf1We6ksiZwzoJ0fLX29yMot5JvNh82OIyJ12OH0HJ5fVHz125Qh7WnVTNNvtZnKksg5Tk4W7ow6t4zAmmRsNi0jICLVzzAMnpi/jez8InqENmZi35ZmR5KLUFkS+Z2begTT0N2Ffcez+W3PCbPjiEgd9Pm6g6zecxIPVyde1vSbQ1BZEvmdhu4ujO0RBGgZARGpfodO5TDjx+Kr3x4fEk5LXy+TE0llqCyJ/MGEPmFYLLAy4Th7j58xO46I1BE22/9Pv/UMa8KEPmFmR5JKUlkS+YPQpl5cG+4HwJw1yeaGEZE64/N1B1iz9yQNXJ156abOOGn6zWGoLImUo+SEy3mbDpOZW2ByGhFxdAdP5jBj8W4AnhjanjBNvzkUlSWRcvRp3ZR2/g3JyS/ifxsOmR1HRByYzWYwZd5WcvKL6NWyCXdEhZkdSS6RypJIOSwWCxP6FI8uzVmbTJGWERCRKvo09gDr9p/C082Zl2/qouk3B6SyJHIBY7q1wMfTlUOnzvJzfJrZcUTEAR04mc3Mc9Nvfx8WTkhTT5MTSVWoLIlcQAM3Z265MgSA2TrRW0QuUfH02zbOFhTRu1UTxvcKNTuSVJHKkkgFoqNCcXaysGbvSXanZpodR0QcyJy1yazX9FudoLIkUoEWPg0Y0sEfgNmrk80NIyIOY/+JbF6MKZ5+m3pdBMFNNP3myFSWRC6iZBmBBVuOkJ6db3IaEantbDaDx+dtJbfARt82Tbm9Z4jZkeQyqSyJXESP0MZ0bOFNXqGNLzccNDuOiNRys9YksyE5HS83Z168UYtP1gUqSyIX8ftlBD5de4CCIpvJiUSkttp3/AwvnZt+e3J4BEGNNf1WF6gsiVTCyC7N8W3oRkpGLkt2ppodR0RqoaJzV7/lFdro18aX2zT9VmeoLIlUgruLM7edu+xXJ3qLSHlmrd7PpgPpNHR34cWbOmOxaPqtrlBZEqmk8b1CcHW2sPFAOtsPZ5gdR0Rqkb3Hz/DykgQAnhoeQQufBiYnkuqksiRSSX7eHgzv1Bwo/hekiAgUT789NncreYU2rmrXjJuvDDY7klQzlSWRS1CyjMD3245yLCvX5DQiUhv897d9bDl4mkbuLsy8oZOm3+oglSWRS9Al2IfuIT4UFBl8sU7LCIjUd3uOZfHq0kQApo2IJFDTb3WSypLIJSoZXfos9iB5hUUmpxERsxQW2Xh07jbyC20MaN+MsT2CzI4kNURlSeQSDe0YQIC3ByfO5LFoW4rZcUTEJB/+tp+th07TyMOFGZp+q9NUlkQukauzE9FRxcsIzFqdjGEYJicSEXtLSsvi9XPTb0+PiKS5VdNvdZnKkkgV3NozBHcXJ7YfyWDzwXSz44iIHRVPv20lv8jGNeF+3HSFpt/qOpUlkSpo4uXG6K4tAPhYi1SK1Csf/LqPbYcz8PZw4YUxmn6rD1SWRKpoQt8wAGJ2pHL09Flzw4iIXSSkZvHvZUkAPDOyAwFWD5MTiT2oLIlUUURzb3q3akKRzeDT2ANmxxGRGlZQZOOxc9Nv14b7cUP3FmZHEjtRWRK5DCXLCHy5/iC5BVpGQKQu++CXvWw/koG1gSsv6Oq3ekVlSeQyDIzwJ6hxA07nFPDtliNmxxGRGhKfksm/fy6efps+KhJ/b02/1ScqSyKXwdnJwp1RYYCWERCpq0qm3wqKDAZF+pde3CH1h8qSyGUad2Uwnm7OJKRlsXbvSbPjiEg1e2/lXnYezcTH05Xnx3TU9Fs9pLIkcpmsDVy5sXvxOiuz1iSbG0ZEqtWuo5m8eW767Z+jOuDXSNNv9ZHKkkg1uLNPGADL4tM4eDLH3DAiUi1Kpt8KbQZDOvgzqkug2ZHEJCpLItWgjV9DrmrXDMOAOWuTzY4jItXgnRV72JWSSWNPV54bravf6jOVJZFqMvHcIpX/23CIM3mF5oYRkcuy82gGby/fA8C/ru9Is0buJicSM6ksiVST/m2b0crXi6y8Qr7ZfNjsOCJSRfmFNh79X/H027COAYzo3NzsSGIylSWRauLkZCm9Bcrs1cnYbFpGQMQRvb08id2pWTTxcuPZ0br6TVSWRKrVDd2DaOTuwr4T2fySdNzsOCJyiXYcyeCdlXsBePb6jvg21PSbqCyJVKuG7i6MuzIYKF6kUkQcR15hEY/N3UqRzWB4p+YM1/SbnKOyJFLN7owKw2KBXxOPs+fYGbPjiEglvfXzHnanZtHUy41/Xd/B7DhSi6gsiVSzkKaeDIzwB2COFqkUcQjbDp/mvV+Kp9+eG92Rppp+k99RWRKpARPPLVI5f/NhMs4WmBtGRCr0++m3EZ2bM6yTpt+kLJUlkRoQ1bop7f0bkZNfxP82HDI7johU4N/LkkhMO4NvQzf+dX1Hs+NILaSyJFIDLBZL6SKVc9Ymk1tQZG4gESnX1kOneb90+q0TTbzcTE4ktZHKkkgNGd2tBY09XTmcfpbr/v0bmw6kmx1JRH4nt6CIR+duxWbAqC6BDO0YYHYkqaVUlkRqiIerM+/efgX+3u7sO5HN2PfX8MKP8RplEqkl3liWxJ5jZ/Bt6M4/R+nqN7kwlSWRGhTVuik/PdyfG7sHYTPgP7/uY/ibv7HloEaZRMy05WA6//m1ePrthTEdaazpN6mAypJIDbN6uvLquC58dGcP/Bq5s/d4Nje+t4YZizXKJGKG3ILiq99sBozp1oLBHTT9JhVTWRKxk2sj/Fk6uT83dGuBzYAPftnHiLdWEXfotNnRROqV15cmsvd4Ns0aufPMyEiz44gDUFkSsSOrpyuv3dyVD+/oQbNG7uw5doYb3l3NizG7ySvUKJNITdt0IJ3//LYPgBljOuHjqek3uTiVJRETDIr0Z+nkqxjdNRCbAe+t3MuIN1exVaNMIjUmt6CIKXO3YhhwQ/cWDIz0NzuSOAiVJRGT+Hi68cYt3fgg+gp8G7qTdOwMN7y3hpeXaJRJpCa8+lMC+05k4+/tzjMjdPWbVJ7KkojJhnQIYOnkqxjVJZAim8E7K/Yy6q3VbD+cYXY0kTpjY/Ip/rtqPwAzbuiE1dPV5ETiSFSWRGqBxl5uvHlrN94f3x3fhm4kpGUx+t3VvPpTAvmFNrPjiTi0s/lFTJm3DcOAm64I4ppwTb/JpXGYspSenk50dDRWqxWr1Up0dDSnT5+ucJ+0tDQmTJhAYGAgnp6eDB06lKSkpNLvnzp1igceeID27dvj6elJSEgIDz74IBkZ+he9mGNox+b8NLk/Izo3p8hm8NbyPYx6exU7juh3UqSqXl6SwP4T2QR4ezBthK5+k0vnMGXptttuIy4ujpiYGGJiYoiLiyM6OvqC2xuGwejRo9m3bx/fffcdW7ZsITQ0lIEDB5KdnQ3A0aNHOXr0KK+88grbt29n9uzZxMTEcPfdd9vrbYmcp4mXG2/f1p13b+9OUy83dqdmMfqd1by2NFGjTCKXILegiHdX7mHWmnPTbzd2wtpA029y6SyGYRhmh7iY+Ph4IiMjiY2NpVevXgDExsYSFRXF7t27ad++/Xn7JCYm0r59e3bs2EGHDsUn8hUVFeHn58eLL77IPffcU+7Pmjt3LuPHjyc7OxsXF5dK5cvMzMRqtZKRkYG3t3cV36XI+U6eyePp73ayaHsKABHNvXllbGc6BFpNTiZSexmGwY/bU5kZE8+hU2cBuK1XCC+M6WRyMqltKvv32yFGltauXYvVai0tSgC9e/fGarWyZs2acvfJy8sDwMPDo/Q5Z2dn3NzcWLVq1QV/VskBq2xREqlJTRu6887t3Xnntu408XIjPiWT699ezRvLEiko0iiTyB9tPXSacR+s5W9fbObQqbP4e7vz6tguPHd9R7OjiQNziEaQmpqKn5/fec/7+fmRmppa7j7h4eGEhoYydepUPvjgA7y8vHjttddITU0lJSWl3H1OnjzJs88+y3333Vdhnry8vNIyBsXNVKQmDe/cnF6tmjDt2x0s3pHKG8uS+GlnGq+M7UJkoEYzRVIyzvJyTALfbDkCgIerE/dd1Zr7+rfC080h/tRJLWbqyNL06dOxWCwVPjZu3AiAxWI5b3/DMMp9HsDV1ZX58+eTmJhIkyZN8PT0ZOXKlQwbNgxnZ+fzts/MzGT48OFERkbyzDPPVJh7xowZpSeaW61WgoODq/DuRS6Nb0N33r29O2/d2o3Gnq7sSsnk+ndW8ebPSRplknorJ7+Q15cmcvUrK0uL0g3dWrDisQFMHtRORUmqhannLJ04cYITJ05UuE1YWBhffPEFjzzyyHlXv/n4+PD6668zceLECl8jIyOD/Px8mjVrRq9evejRowfvvPNO6fezsrIYMmQInp6e/PDDD2Wm7spT3shScHCwzlkSuzmelcdT325nyc40ADq28OaVsV0ID9Dvn9QPNpvBgi1HeGnJbtIyiz+Pe4Q2ZtqISLoE+5gbThxGZc9ZcqgTvNetW0fPnj0BWLduHb17977gCd7lSUpKIjw8nMWLFzN48GCg+EANGTIEd3d3fvzxRzw9PS85n07wFjMYhsHCrUd5ZuFOTucU4Ops4aFr23J//9a4ODvE6YgiVbJ+/ymeW7SLbecWbg1q3ICpwyK4rlPABWcbRMpTp8oSwLBhwzh69CgffPABAH/+858JDQ3l+++/L90mPDycGTNmMGbMGKD4yrZmzZoREhLC9u3beeihh7jiiiuYP38+UDyiNGjQIHJycliwYAFeXl6lr9WsWbNyp+vKo7IkZjqWlcs/Fuxg6a7iUaZOLay8MrYL7QMamZxMpHodPJnDzJh4ftxefK5qQ3cXJl3Thgl9wvBwrdzntcjvVfbvt8NM5n7++ec8+OCDpSNCo0aN4u233y6zTUJCQpkFJVNSUnjkkUdIS0ujefPm3HHHHUybNq30+5s2bWLdunUAtGnTpsxr7d+/n7CwsBp6NyLVx6+RB/+JvoLv4opHmbYfyWDkW6t4aGBb7ruqlUaZxOFl5hbwzvI9zFqdTH6RDScL3NIzhEcGtcO3obvZ8aQecJiRpdpMI0tSWxzLzOXJBdtZFn8MgC5BxaNMbf01yiSOp7DIxlcbDvH60kROZucD8Ke2vvxjeITOz5NqUeem4WozlSWpTQyj+MTX6Qt3kplbiJuzE5MHtePeP7XUKJM4jF8Tj/Pcol0kpp0BoFUzL54aHsHV7f10XpJUG5UlO1JZktooLTOXqd9sZ/nuc6NMwT68OrYzbfw0yiS1155jWTy/KJ4VCccB8PF05eFr23J771BcVfalmqks2ZHKktRWhmEwf/MR/vn9TrJyC3FzceLRQe2450+tcHbSv86l9kjPzueNZYl8tu4gRTYDFycLd0SF8eC1bfDxdDM7ntRRKkt2pLIktV1qRi5//2YbK8/9a71biA8v39SFNn4NTU4m9V1+oY1P1ibz5s9JZOYWAjAwwp8nrwunVTP9fkrNUlmyI5UlcQSGYTB302Ge/X4XWXnFo0yPDW7H3f00yiT2ZxgGS3elMWPxbvafyAYgPKART4+IpE8bX5PTSX2hsmRHKkviSFIyzvL3+dv5JbF4lKl7iA+vjO2if8WL3ew8msFzP8Szdt9JoPhWPo8NbsfYHsEq7mJXKkt2pLIkjsYwDP638RDP/RBPVl4h7i5OTBnSnol9W+qPldSYY5m5vPJTAnM3HcYwwM3FiXv6teSvV7ehobvDLPsndYjKkh2pLImjOnr6LE/M38ZvScX3aOwR2piXx3ahpa/XRfYUqbzcgiL++9s+3l25l5z8IgBGdG7OE0PDCW5y6beYEqkuKkt2pLIkjswwDL7acIjnF8VzJq8QD1cnpgwJZ2KfMJw0yiSXoeT+hS/FJHDk9FkAugb7MG1EJFeENjY5nYjKkl2pLEldcOT0WZ6Yt41Ve4pHmXqGNeGlmzoTplEmqYLNB9N59oddbDl4GoBAqwdPDAtnZOdAlXCpNVSW7EhlSeoKwzD4Yv1BXlgUT3Z+ER6uTjwxNJw7ozTKJJVz5PRZXly8m4VbjwLg6ebMX/q35p4/taKBm252K7WLypIdqSxJXXPoVA5PzN/Gmr3FVyv1bNmEl2/qTGhTjTJJ+bLzCnlv5V4+/G0feYU2LBa4qXsQjw1pj7+3h9nxRMqlsmRHKktSF9lsBp+vP8iMH+PJyS+igaszfx8WTnTvUI0ySakim8H8TYd5+acEjmflAdCrZROmjYikYwuryelEKqayZEcqS1KXHTqVw+PztpWuidO7VRNeurELIU11FVN9t3bvSZ79YRe7UjIBCG3qydRhEQzp4K+b3YpDUFmyI5UlqetsNoPP1h1gxo+7OVtQhKebM1OHhXN7L40y1UfJJ7J54cd4ftqVBkAjDxcevKYtd/QJxd1F5yWJ41BZsiOVJakvDp7MYcq8razbfwqAqFZNeemmzlorp57IOFvAWz8nMWdtMgVFBs5OFm7vFcLDA9vRxEs3uxXHo7JkRypLUp/YbAafxh5g5uLiUSYvN2emXhfB7b1CNPVSRxUW2fhi/UFeX5pIek4BAAPaN+Mf10XQ1r+RyelEqk5lyY5UlqQ+OnAymylzt7E+uXiUqW+bprx4Y2eCGmuUqS5ZkXCM5xfFs+fYGQDa+jXkqRGR9G/XzORkIpdPZcmOVJakvrLZDOasTebFmN3kFtjwcnPmyeER3NZTo0yOLjEti+cWxfPruRsuN/FyY/Kgdtx6ZTAuzk4mpxOpHipLdqSyJPXd/hPZPD5vKxuS0wH4U1tfZt7YmRY+DUxOJpfq5Jk8XluayJfrD2IzwNXZwsS+Lfnb1W2wNnA1O55ItVJZsiOVJZHi9XZmr0nmpZjd5BXaaOjuwj+GR3DLlcEaZXIAeYVFzF6dzNvL95CVVwjA0A4BTL0uXIuRSp2lsmRHKksi/2/f8TNMmbeNTQeKR5muateMmTd0IlCjTLWSYRjE7EhlxuLdHDyVA0DHFt48NTyS3q2ampxOpGapLNmRypJIWUU2g1mr9/PykgTyCm00cnfhqRERjOuhUabaZPvhDJ5dtIv155aC8GvkzpQh7bmxe5DWz5J6QWXJjlSWRMq39/gZHpu7tfTO8/3bNWPmjZ1obtUok5nSMnN5KSaBb7YcxjDAw9WJP/+pFff1b42Xu4vZ8UTsRmXJjlSWRC6syGbw0ap9vPJTIvmFNhp5uDBtRCRjrwjSKJOdnc0v4j+/7uP9X/ZytqAIgNFdA3l8aLimSaVeUlmyI5UlkYvbcyyLx+ZuI+7QaQCubt+MGTd0JsCqO9LXNJvN4LutR3gpJoGUjFwArghtzLQRkXQN9jE3nIiJVJbsSGVJpHIKi2z8d9V+Xvspkfyi4lGmZ0Z24MbuLTTKVEM2Jp/i2R92sfVwBgAtfBrw92HhjOjcXMdc6j2VJTtSWRK5NElpWTw2d2vpH/Brw/144YZO+HtrlKm6HDqVw8zFu1m0PQWAhu4u/PXq1tzVtyUerrrZrQioLNmVypLIpSsssvGf3/bxxtIk8otseHu4MH1UB8Z00yjT5cjKLeCdFXv5ePV+8gttOFng5iuDeWRQe5o1cjc7nkitorJkRypLIlWXmJbFo//byvYjxaNMAyP8eWFMR/w0ynRJimwGX284xGtLEzhxJh8ovl/fU8MjiWiuzyWR8qgs2ZHKksjlKSyy8cGv+3hjWSIFRQbWBq78c1QHru8aqFGmSliVdILnFu1id2oWAK18vXjyugiujfDT8ROpgMqSHaksiVSP3amZPDZ3KzuOZAIwONKf58Z0xK+RRpnKs/f4GV5YFM/Pu48BYG3gykPXtmV871DcXHSzW5GLUVmyI5UlkepTUGTj/ZV7eXN5EgVFBj6exaNMo7polKnE6Zx83liWxGexByi0Gbg4WRjfO5SHB7bFx9PN7HgiDkNlyY5UlkSqX3xKJo/+byu7UopHmYZ08Oe50Z3q9UnKBUU2Pl17gH//nETG2QIABkb4MfW6CFo3a2hyOhHHo7JkRypLIjWjoMjGuyv28tbyJAptBo09XfnX9R3r3RpBhmHwc/wxXvgxnn0nsgEID2jEU8Mj6dfW1+R0Io5LZcmOVJZEatbOoxk8Nncb8edGmYZ1DODZ0R3xbVj3R5niUzJ5btEuVu85CYBvQzceHdyecT2CcdbNbkUui8qSHaksidS8/EIb76zYwzsr9lBoM2ji5caz13dkeOfmZkerEcez8nhtaQJfbziEzQA3Fyfu7teSvw5oTSMPV7PjidQJKkt2pLIkYj87jmTw2NytpZfJD+/UnH9d34GmdWSUKbegiI9X7+fdFXs5k1cIwPDOzfn70HCCm3ianE6kblFZsiOVJRH7yi+08fbyJN5ZuZcim0FTLzeeG92RYZ0cd5TJMAx+2JbCzMW7OXL6LABdgqxMGxFJj7AmJqcTqZtUluxIZUnEHNsPF48yJaQVjzKN6Nycf13fkSZejnX5fNyh0zz7wy42HUgHoLnVg8eHtuf6Li1w0nlJIjVGZcmOVJZEzJNXWMRbP+/hvV+KR5l8G7rx3OhODO0YYHa0izp6+iwvxezm27ijADRwdeb+/q3581WtaOCmm92K1DSVJTtSWRIx37bDp3ls7lYS084AMKpLIP8c1YHGtXCUKTuvkA9+2ct/fttHboENgBu7BzFlSHsCrFqtXMReVJbsSGVJpHbIKyzi38uSeP+XvdgM8G3ozgtjOjK4Q+0YZbLZDOZvPszLSxI4lpUHQM+wJkwbEUmnIKvJ6UTqH5UlO1JZEqld4g4VjzLtOVY8yjS6ayDTR3Uw9VYg6/ad5NlFu0rvexfSxJOpw8IZ2jGgXi2wKVKbqCzZkcqSSO2TW1DEG8uS+M+vxaNMzRq588KYTgyK9LdrjgMns5nx425idqYC0MjdhUnXtGFC3zDcXXRekoiZVJbsSGVJpPbacjCdx+ZuZe/x4tuE3NCtBc+M7IDVs2YXdszMLeDt5XuYvTqZ/CIbTha4rVcIkwe2qzNrQok4OpUlO1JZEqndcguKeH1pIh/+tg+bAX6N3JlxQyeujaj+UabCIhtfbjjE60sTOZWdD8BV7Zrx1PAI2vk3qvafJyJVp7JkRypLIo5h04F0pszdWnoz2hu7B/H0yEisDapnlOmXxOM8v2hX6RV5bfwa8o/hEVzd3q9aXl9EqpfKkh2pLIk4jtyCIl79KYH/rtqPYYC/tzszb+jM1eFVLzRJaVk8tyieXxKPA9DY05XJg9pxa88QXJ2dqiu6iFQzlSU7UlkScTwbk08xZd429p8bZRp7RRBPjbi0UaZT2fm8vjSRL9YfpMhm4Ops4c6oMB64pm2NnxMlIpdPZcmOVJZEHNPZ/CJe+SmBj1cXjzIFeHsw88ZODLjItFl+oY05a5J5c3kSWbnFN7sdHOnP1OsiaOnrZY/oIlINVJbsSGVJxLFtSD7FlLlbST6ZA8DNPYL5x4gIvD3Kjg4ZhsGSnWnMWBzPgXPbRjb35qkREfRp7Wv33CJyeVSW7EhlScTxnc0v4qUlu5m9JhnDKL6Z7Ys3duaqds0A2HEkg+cW7SJ23ymgeN2mKUPac2P3IJx1s1sRh6SyZEcqSyJ1x/r9p5gyb2vpyNEtVwZTZDOYt/kwhgHuLk78+apW3N+/NV7uLianFZHLobJkRypLInVLTn4hL8UkMHtNcpnnr+8ayONDw2nh08CcYCJSrSr791v/LBIR+QNPNxemj+rA0I4BPPPdTqyervx9WDjdQxqbHU1ETKCRpWqgkSURERHHU9m/31otTURERKQCKksiIiIiFVBZEhEREamAypKIiIhIBVSWRERERCqgsiQiIiJSAYcpS+np6URHR2O1WrFarURHR3P69OkK90lLS2PChAkEBgbi6enJ0KFDSUpKKndbwzAYNmwYFouFb7/9tvrfgIiIiDgkhylLt912G3FxccTExBATE0NcXBzR0dEX3N4wDEaPHs2+ffv47rvv2LJlC6GhoQwcOJDs7Ozztn/jjTewWHR/JxERESnLIVbwjo+PJyYmhtjYWHr16gXAhx9+SFRUFAkJCbRv3/68fZKSkoiNjWXHjh106NABgHfffRc/Pz++/PJL7rnnntJtt27dymuvvcaGDRto3ry5fd6UiIiIOASHGFlau3YtVqu1tCgB9O7dG6vVypo1a8rdJy8vDwAPD4/S55ydnXFzc2PVqlWlz+Xk5HDrrbfy9ttvExAQUEPvQERERByVQ5Sl1NRU/Pz8znvez8+P1NTUcvcJDw8nNDSUqVOnkp6eTn5+PjNnziQ1NZWUlJTS7SZPnkyfPn24/vrrK50nLy+PzMzMMg8RERGpm0wtS9OnT8disVT42LhxI0C55xMZhnHB84xcXV2ZP38+iYmJNGnSBE9PT1auXMmwYcNwdnYGYOHChSxfvpw33njjknLPmDGj9ERzq9VKcHDwpb1xERERcRimnrM0adIkbrnllgq3CQsLY9u2baSlpZ33vePHj+Pv73/Bfa+44gri4uLIyMggPz+fZs2a0atXL3r06AHA8uXL2bt3Lz4+PmX2u/HGG/nTn/7EypUry33dqVOn8sgjj5R+nZmZqcIkIiJSR1kMwzDMDnEx8fHxREZGsm7dOnr27AnAunXr6N27N7t37y73BO/yJCUlER4ezuLFixk8eDCpqamcOHGizDadOnXi3//+NyNHjqRly5aVet3K3rVYREREao/K/v12iKvhIiIiGDp0KPfeey8ffPABAH/+858ZMWJEmaIUHh7OjBkzGDNmDABz586lWbNmhISEsH37dh566CFGjx7N4MGDAQgICCj3pO6QkJBKFyUong4EdO6SiIiIAyn5u32xcSOHKEsAn3/+OQ8++GBp0Rk1ahRvv/12mW0SEhLIyMgo/TolJYVHHnmEtLQ0mjdvzh133MG0adOqPVtWVhaApuJEREQcUFZWFlar9YLfd4hpuNrOZrNx9OhRGjVqVCsWtiw5h+rQoUOaFkTH4490PM6nY1KWjkdZOh5l1aXjYRgGWVlZBAYG4uR04WveHGZkqTZzcnIiKCjI7Bjn8fb2dvhf5Oqk41GWjsf5dEzK0vEoS8ejrLpyPCoaUSrhEOssiYiIiJhFZUlERESkAipLdZC7uzvPPPMM7u7uZkepFXQ8ytLxOJ+OSVk6HmXpeJRVH4+HTvAWERERqYBGlkREREQqoLIkIiIiUgGVJREREZEKqCw5sCNHjjB+/HiaNm2Kp6cnXbt2ZdOmTaXfNwyD6dOnExgYSIMGDRgwYAA7d+40MXHNquh4FBQU8MQTT9CpUye8vLwIDAzkjjvu4OjRoyanrlkX+x35vfvuuw+LxcIbb7xh35B2VJnjER8fz6hRo7BarTRq1IjevXtz8OBBkxLXrIsdjzNnzjBp0iSCgoJo0KABERERvPfeeyYmrjlhYWFYLJbzHn/729+A+vd5WtHxqI+fpypLDio9PZ2+ffvi6urK4sWL2bVrF6+++io+Pj6l27z00ku89tprvP3222zYsIGAgAAGDRpUenuWuuRixyMnJ4fNmzczbdo0Nm/ezDfffENiYiKjRo0yN3gNqszvSIlvv/2WdevWERgYaP+gdlKZ47F371769etHeHg4K1euZOvWrUybNg0PDw/zgteQyhyPyZMnExMTw2effUZ8fDyTJ0/mgQce4LvvvjMveA3ZsGEDKSkppY+lS5cCMHbsWKB+fZ5CxcejPn6eYohDeuKJJ4x+/fpd8Ps2m80ICAgwZs6cWfpcbm6uYbVajffff98eEe3qYsejPOvXrzcA48CBAzWUylyVPSaHDx82WrRoYezYscMIDQ01Xn/99ZoPZ4LKHI+bb77ZGD9+vJ0Smasyx6NDhw7Gv/71rzLPde/e3XjqqadqMlqt8NBDDxmtW7c2bDZbvfs8Lc/vj0d56vrnqUaWHNTChQvp0aMHY8eOxc/Pj27duvHhhx+Wfn///v2kpqaW3ngYitfG6N+/P2vWrDEjco262PEoT0ZGBhaLpdyRlrqgMsfEZrMRHR3NlClT6NChg0lJ7eNix8Nms7Fo0SLatWvHkCFD8PPzo1evXnz77bfmha5Blfn96NevHwsXLuTIkSMYhsGKFStITExkyJAhJqW2j/z8fD777DPuuusuLBZLvfs8/aM/Ho/y1PXPU40sOSh3d3fD3d3dmDp1qrF582bj/fffNzw8PIw5c+YYhmEYq1evNgDjyJEjZfa79957jcGDB5sRuUZd7Hj80dmzZ40rrrjCuP322+2c1H4qc0xeeOEFY9CgQaX/WqzLI0sXOx4pKSkGYHh6ehqvvfaasWXLFmPGjBmGxWIxVq5caXL66leZ34+8vDzjjjvuMADDxcXFcHNzMz755BMTU9vH119/bTg7O5d+fta3z9M/+uPx+KP68HmqsuSgXF1djaioqDLPPfDAA0bv3r0Nw/j//3MfPXq0zDb33HOPMWTIELvltJeLHY/fy8/PN66//nqjW7duRkZGhr0i2t3FjsnGjRsNf3//Mh+AdbksXex4HDlyxACMW2+9tcw2I0eONG655Ra75bSXyvx/5uWXXzbatWtnLFy40Ni6davx1ltvGQ0bNjSWLl1q77h2NXjwYGPEiBGlX9e3z9M/+uPx+L368nmqaTgH1bx5cyIjI8s8FxERUXrVTkBAAACpqalltjl27Bj+/v72CWlHFzseJQoKChg3bhz79+9n6dKldeKO2RdysWPy22+/cezYMUJCQnBxccHFxYUDBw7w6KOPEhYWZkLimnWx4+Hr64uLi0ulfo/qgosdj7Nnz/Lkk0/y2muvMXLkSDp37sykSZO4+eabeeWVV8yIbBcHDhxg2bJl3HPPPaXP1bfP098r73iUqE+fpypLDqpv374kJCSUeS4xMZHQ0FAAWrZsSUBAQOkVDFA87/zLL7/Qp08fu2a1h4sdD/j//2MnJSWxbNkymjZtau+YdnWxYxIdHc22bduIi4srfQQGBjJlyhSWLFliRuQadbHj4ebmxpVXXnnR36O64mLHo6CggIKCApycyv6ZcHZ2xmaz2S2nvc2aNQs/Pz+GDx9e+lx9+zz9vfKOB9S/z1NNwzmo9evXGy4uLsbzzz9vJCUlGZ9//rnh6elpfPbZZ6XbzJw507BarcY333xjbN++3bj11luN5s2bG5mZmSYmrxkXOx4FBQXGqFGjjKCgICMuLs5ISUkpfeTl5ZmcvmZU5nfkj+ryNFxljsc333xjuLq6Gv/5z3+MpKQk46233jKcnZ2N3377zcTkNaMyx6N///5Ghw4djBUrVhj79u0zZs2aZXh4eBjvvvuuiclrTlFRkRESEmI88cQT532vPn2elrjQ8aiPn6cqSw7s+++/Nzp27Gi4u7sb4eHhxn/+858y37fZbMYzzzxjBAQEGO7u7sZVV11lbN++3aS0Na+i47F//34DKPexYsUK80LXsIv9jvxRXS5LhlG54/HRRx8Zbdq0MTw8PIwuXboY3377rQlJ7eNixyMlJcWYMGGCERgYaHh4eBjt27c3Xn311QtePu7olixZYgBGQkLCed+rb5+nhnHh41EfP08thmEYZoxoiYiIiDgCnbMkIiIiUgGVJREREZEKqCyJiIiIVEBlSURERKQCKksiIiIiFVBZEhEREamAypKIiIhIBVSWRERERCqgsiQi9VpYWBhvvPGG2TFEpBZTWRIRhzVy5EgGDhxY7vfWrl2LxWJh8+bNdk4lInWNypKIOKy7776b5cuXc+DAgfO+9/HHH9O1a1e6d+9uQjIRqUtUlkTEYY0YMQI/Pz9mz55d5vmcnBy+/vpr7r77bubPn0+HDh1wd3cnLCyMV1999YKvl5ycjMViIS4urvS506dPY7FYWLlyJQArV67EYrGwZMkSunXrRoMGDbjmmms4duwYixcvJiIiAm9vb2699VZycnJKX8cwDF566SVatWpFgwYN6NKlC/PmzavOwyEiNURlSUQclouLC3fccQezZ8/m9/cEnzt3Lvn5+URFRTFu3DhuueUWtm/fzvTp05k2bdp55aoqpk+fzttvv82aNWs4dOgQ48aN44033uCLL75g0aJFLF26lLfeeqt0+6eeeopZs2bx3nvvsXPnTiZPnsz48eP55ZdfLjuLiNQsi/H7TxgREQeze/duIiIiWL58OVdffTUA/fv3p0WLFlgsFo4fP85PP/1Uuv3jjz/OokWL2LlzJ1B8gvfDDz/Mww8/THJyMi1btmTLli107doVKB5Zaty4MStWrGDAgAGsXLmSq6++mmXLlnHttdcCMHPmTKZOncrevXtp1aoVAPfffz/JycnExMSQnZ2Nr68vy5cvJyoqqjTLPffcQ05ODl988YU9DpWIVJFGlkTEoYWHh9OnTx8+/vhjAPbu3ctvv/3GXXfdRXx8PH379i2zfd++fUlKSqKoqOiyfm7nzp1L/7u/vz+enp6lRankuWPHjgGwa9cucnNzGTRoEA0bNix9fPLJJ+zdu/eycohIzXMxO4CIyOW6++67mTRpEu+88w6zZs0iNDSUa6+9FsMwsFgsZbataDDdycnpvG0KCgrK3dbV1bX0v1ssljJflzxns9kASv9z0aJFtGjRosx27u7uF3t7ImIyjSyJiMMbN24czs7OfPHFF8yZM4eJEydisViIjIxk1apVZbZds2YN7dq1w9nZ+bzXadasGQApKSmlz/3+ZO+qioyMxN3dnYMHD9KmTZsyj+Dg4Mt+fRGpWRpZEhGH17BhQ26++WaefPJJMjIymDBhAgCPPvooV155Jc8++yw333wza9eu5e233+bdd98t93UaNGhA7969mTlzJmFhYZw4cYKnnnrqsvM1atSIxx57jMmTJ2Oz2ejXrx+ZmZmsWbOGhg0bcuedd172zxCRmqORJRGpE+6++27S09MZOHAgISEhAHTv3p3//e9/fPXVV3Ts2JGnn36af/3rX6Vlqjwff/wxBQUF9OjRg4ceeojnnnuuWvI9++yzPP3008yYMYOIiAiGDBnC999/T8uWLavl9UWk5uhqOBEREZEKaGRJREREpAIqSyIiIiIVUFkSERERqYDKkoiIiEgFVJZEREREKqCyJCIiIlIBlSURERGRCqgsiYiIiFRAZUlERESkAipLIiIiIhVQWRIRERGpgMqSiIiISAX+D3b7kpKnWHVfAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "delayed_object.pull()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/python_workflow_definition/src/python_workflow_definition/aiida.py b/python_workflow_definition/src/python_workflow_definition/aiida.py index 16a643f..dc9e8e6 100644 --- a/python_workflow_definition/src/python_workflow_definition/aiida.py +++ b/python_workflow_definition/src/python_workflow_definition/aiida.py @@ -101,10 +101,12 @@ def write_workflow_json(wg, file_name): raw_value = input.value.get_list() elif isinstance(input.value, orm.Dict): raw_value = input.value.get_dict() + # unknow reason, there is a key "node_type" in the dict + raw_value.pop("node_type", None) else: raw_value = input.value.value - input_node_name = str(i) - data["nodes"][input_node_name] = raw_value + data["nodes"][str(i)] = raw_value + input_node_name = i data_node_name_mapping[input.value.uuid] = input_node_name i += 1 else: @@ -115,7 +117,6 @@ def write_workflow_json(wg, file_name): "sn": input_node_name, "sh": None }) - with open(file_name, "w") as f: # json.dump({"nodes": data[], "edges": edges_new_lst}, f) json.dump(data, f, indent=2) diff --git a/universal_workflow_qe.ipynb b/universal_workflow_qe.ipynb index 0367aa7..dd9512c 100644 --- a/universal_workflow_qe.ipynb +++ b/universal_workflow_qe.ipynb @@ -16,16 +16,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Profile" + "Profile" ] }, - "execution_count": 1, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -38,22 +38,22 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "wg = load_workflow_json(file_name='workflow_qe.json')" + "wg = load_workflow_json(file_name='aiida_qe.json')" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a884a79ffbd548c79c0f67ebabd4a152", + "model_id": "a22b1fa6af494c348ca8f4ebf0255751", "version_major": 2, "version_minor": 1 }, @@ -61,7 +61,7 @@ "NodeGraphWidget(settings={'minimap': True}, style={'width': '90%', 'height': '600px'}, value={'name': 'WorkGra…" ] }, - "execution_count": 3, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -72,349 +72,72 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "03/24/2025 12:18:59 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: pickle_node10,pickle_node11,pickle_node12,pickle_node13,pickle_node15,pickle_node16,pickle_node17,pickle_node18,pickle_node19,pickle_node20,pickle_node22,pickle_node23,pickle_node25,pickle_node27,pickle_node29\n", - "03/24/2025 12:20:42 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1138, 1145, 1152, 1159, 1166, 1173, 1180, 1187, 1194, 1201, 1208, 1215, 1222, 1229, 1236\n", - "03/24/2025 12:25:34 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node10, type: PYTHONJOB, finished.\n", - "03/24/2025 12:25:37 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node11, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:39 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node12, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:41 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node13, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:43 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node15, type: PYTHONJOB, finished.\n" + "04/01/2025 06:19:04 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_bulk_structure1\n", + "04/01/2025 06:19:05 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_bulk_structure1, type: PyFunction, finished.\n", + "04/01/2025 06:19:05 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict2\n", + "04/01/2025 06:19:07 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict2, type: PyFunction, finished.\n", + "04/01/2025 06:19:07 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe3\n", + "[thinkpad:240834] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", + "04/01/2025 06:19:56 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['energy', 'volume'])\n", + "04/01/2025 06:19:57 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe3, type: PyFunction, finished.\n", + "04/01/2025 06:19:57 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: generate_structures4\n", + "04/01/2025 06:19:59 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: generate_structures4, type: PyFunction, finished.\n", + "04/01/2025 06:19:59 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict7,get_dict9,get_dict11,get_dict13,get_dict15\n", + "04/01/2025 06:20:00 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict7, type: PyFunction, finished.\n", + "04/01/2025 06:20:00 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe8,get_dict9,get_dict11,get_dict13,get_dict15\n", + "[thinkpad:241673] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", + "04/01/2025 06:20:08 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 06:20:09 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe8, type: PyFunction, finished.\n", + "04/01/2025 06:20:09 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict9,get_dict11,get_dict13,get_dict15\n", + "04/01/2025 06:20:10 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict9, type: PyFunction, finished.\n", + "04/01/2025 06:20:10 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe10,get_dict11,get_dict13,get_dict15\n", + "[thinkpad:241817] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", + "04/01/2025 06:20:18 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 06:20:18 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe10, type: PyFunction, finished.\n", + "04/01/2025 06:20:18 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict11,get_dict13,get_dict15\n", + "04/01/2025 06:20:20 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict11, type: PyFunction, finished.\n", + "04/01/2025 06:20:20 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe12,get_dict13,get_dict15\n", + "[thinkpad:242008] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", + "04/01/2025 06:20:32 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 06:20:32 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe12, type: PyFunction, finished.\n", + "04/01/2025 06:20:32 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict13,get_dict15\n", + "04/01/2025 06:20:34 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict13, type: PyFunction, finished.\n", + "04/01/2025 06:20:34 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe14,get_dict15\n", + "[thinkpad:242195] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", + "04/01/2025 06:20:48 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 06:20:49 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe14, type: PyFunction, finished.\n", + "04/01/2025 06:20:49 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict15\n", + "04/01/2025 06:20:51 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict15, type: PyFunction, finished.\n", + "04/01/2025 06:20:51 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe16\n", + "[thinkpad:242533] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", + "04/01/2025 06:21:04 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 06:21:05 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe16, type: PyFunction, finished.\n", + "04/01/2025 06:21:05 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list5,get_list6\n", + "04/01/2025 06:21:07 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_list5, type: PyFunction, finished.\n", + "04/01/2025 06:21:07 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list6\n", + "04/01/2025 06:21:08 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_list6, type: PyFunction, finished.\n", + "04/01/2025 06:21:08 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: plot_energy_volume_curve17\n", + "04/01/2025 06:21:09 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: plot_energy_volume_curve17, type: PyFunction, finished.\n", + "04/01/2025 06:21:10 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", + "04/01/2025 06:21:10 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|finalize]: Finalize workgraph.\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:46 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node16, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:48 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node17, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:50 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node18, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:52 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node19, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:55 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node20, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:57 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node22, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:25:59 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node23, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:26:02 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node25, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:26:04 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node27, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:26:06 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: pickle_node29, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:26:12 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_bulk_structure1\n", - "03/24/2025 12:26:15 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1303\n", - "03/24/2025 12:26:30 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: get_bulk_structure1, type: PYTHONJOB, finished.\n", - "03/24/2025 12:26:36 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict14\n", - "03/24/2025 12:26:38 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1314\n", - "03/24/2025 12:26:49 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: get_dict14, type: PYTHONJOB, finished.\n", - "03/24/2025 12:26:55 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe2\n", - "03/24/2025 12:26:57 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1325\n", - "03/24/2025 12:27:34 PM <270803> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['energy', 'volume'])\n", - "03/24/2025 12:27:35 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: calculate_qe2, type: PYTHONJOB, finished.\n", - "03/24/2025 12:27:40 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: generate_structures3\n", - "03/24/2025 12:27:43 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1336\n", - "03/24/2025 12:27:54 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: generate_structures3, type: PYTHONJOB, finished.\n", - "03/24/2025 12:27:59 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict21,get_dict24,get_dict26,get_dict28,get_dict30\n", - "03/24/2025 12:28:07 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1351, 1358, 1365, 1372, 1379\n", - "03/24/2025 12:28:34 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: get_dict21, type: PYTHONJOB, finished.\n", - "03/24/2025 12:28:35 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: get_dict24, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:28:35 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: get_dict26, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:28:36 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: get_dict28, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:28:36 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: get_dict30, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:28:41 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe4,calculate_qe5,calculate_qe6,calculate_qe7,calculate_qe8\n", - "03/24/2025 12:28:51 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1406, 1413, 1420, 1427, 1434\n", - "03/24/2025 12:29:22 PM <270803> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/24/2025 12:29:23 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: calculate_qe4, type: PYTHONJOB, finished.\n", - "03/24/2025 12:29:29 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "03/24/2025 12:29:30 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1413, 1420, 1427, 1434\n", - "03/24/2025 12:29:38 PM <270803> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/24/2025 12:29:39 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: calculate_qe5, type: PYTHONJOB, finished.\n", - "03/24/2025 12:29:45 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "03/24/2025 12:29:47 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1420, 1427, 1434\n", - "03/24/2025 12:29:55 PM <270803> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/24/2025 12:29:55 PM <270803> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/24/2025 12:29:56 PM <270803> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/24/2025 12:29:56 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: calculate_qe6, type: PYTHONJOB, finished.\n", - "03/24/2025 12:29:57 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: calculate_qe7, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:29:57 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: calculate_qe8, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:30:02 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list31,get_list32\n", - "03/24/2025 12:30:07 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1466, 1473\n", - "03/24/2025 12:30:22 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: get_list31, type: PYTHONJOB, finished.\n", - "03/24/2025 12:30:23 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: get_list32, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/24/2025 12:30:28 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: plot_energy_volume_curve9\n", - "03/24/2025 12:30:30 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1488\n", - "03/24/2025 12:30:42 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|update_task_state]: Task: plot_energy_volume_curve9, type: PYTHONJOB, finished.\n", - "03/24/2025 12:30:47 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "03/24/2025 12:30:48 PM <270803> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [1131|WorkGraphEngine|finalize]: Finalize workgraph.\n" - ] + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ From c9b3caf8391e579edfe4697cccbd3f72458cf8b2 Mon Sep 17 00:00:00 2001 From: superstar54 Date: Tue, 1 Apr 2025 10:59:37 +0200 Subject: [PATCH 3/7] Run the relevant notebooks --- aiida_qe.ipynb | 1062 +++++++++++------------ jobflow_qe.ipynb | 1365 +++++++++++------------------ pyiron_base_qe.ipynb | 562 +++--------- universal_workflow_qe.ipynb | 1629 +++++++++++++++++------------------ 4 files changed, 1955 insertions(+), 2663 deletions(-) diff --git a/aiida_qe.ipynb b/aiida_qe.ipynb index 621323a..25aaf06 100644 --- a/aiida_qe.ipynb +++ b/aiida_qe.ipynb @@ -230,7 +230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e2cf9fa0e97e4cd2b074dc33ba0f23a6", + "model_id": "dcec02cd916542798a729a20a32242dd", "version_major": 2, "version_minor": 1 }, @@ -719,142 +719,142 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-04-01 09:38:07,741 INFO Started executing jobs locally\n", - "2025-04-01 09:38:08,025 INFO Starting job - get_bulk_structure (4e19cf85-8556-41e9-8a66-7970c772779d)\n", - "2025-04-01 09:38:08,034 INFO Finished job - get_bulk_structure (4e19cf85-8556-41e9-8a66-7970c772779d)\n", - "2025-04-01 09:38:08,038 INFO Starting job - get_dict (332fbe1b-bfda-495a-9184-37aed1353865)\n", - "2025-04-01 09:38:08,046 INFO Finished job - get_dict (332fbe1b-bfda-495a-9184-37aed1353865)\n", - "2025-04-01 09:38:08,049 INFO Starting job - calculate_qe (9bf70cee-1283-468a-b4f4-fcc9f16129f7)\n" + "2025-04-01 10:50:22,650 INFO Started executing jobs locally\n", + "2025-04-01 10:50:22,916 INFO Starting job - get_bulk_structure (cbd7d9e1-e367-4d7c-94f0-310b1a4f9c8f)\n", + "2025-04-01 10:50:22,919 INFO Finished job - get_bulk_structure (cbd7d9e1-e367-4d7c-94f0-310b1a4f9c8f)\n", + "2025-04-01 10:50:22,920 INFO Starting job - get_dict (5c7d171f-7910-4ce6-9eef-9a4d9a7529a8)\n", + "2025-04-01 10:50:22,924 INFO Finished job - get_dict (5c7d171f-7910-4ce6-9eef-9a4d9a7529a8)\n", + "2025-04-01 10:50:22,925 INFO Starting job - calculate_qe (8267d4c7-23ef-4ed3-9440-841790f572b7)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[thinkpad:375970] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2025-04-01 09:39:22,406 INFO Finished job - calculate_qe (9bf70cee-1283-468a-b4f4-fcc9f16129f7)\n", - "2025-04-01 09:39:22,408 INFO Starting job - generate_structures (87a28cb9-ef88-4537-a70d-9b34a37a726c)\n", - "2025-04-01 09:39:22,434 INFO Finished job - generate_structures (87a28cb9-ef88-4537-a70d-9b34a37a726c)\n", - "2025-04-01 09:39:22,437 INFO Starting job - get_dict (2a66ebc1-8ce6-417a-8b9b-77331dbf22ce)\n", - "2025-04-01 09:39:22,446 INFO Finished job - get_dict (2a66ebc1-8ce6-417a-8b9b-77331dbf22ce)\n", - "2025-04-01 09:39:22,449 INFO Starting job - get_dict (ddfa847d-a0b2-4cbe-b254-e5841880b426)\n", - "2025-04-01 09:39:22,455 INFO Finished job - get_dict (ddfa847d-a0b2-4cbe-b254-e5841880b426)\n", - "2025-04-01 09:39:22,458 INFO Starting job - get_dict (fe348067-207b-40ee-be46-eaa8b55a3047)\n", - "2025-04-01 09:39:22,469 INFO Finished job - get_dict (fe348067-207b-40ee-be46-eaa8b55a3047)\n", - "2025-04-01 09:39:22,472 INFO Starting job - get_dict (76a6883a-bac4-4fad-b1d9-fa0236ec3f8d)\n", - "2025-04-01 09:39:22,478 INFO Finished job - get_dict (76a6883a-bac4-4fad-b1d9-fa0236ec3f8d)\n", - "2025-04-01 09:39:22,480 INFO Starting job - get_dict (15907897-f7e6-4639-9876-42ed4dadaae1)\n", - "2025-04-01 09:39:22,490 INFO Finished job - get_dict (15907897-f7e6-4639-9876-42ed4dadaae1)\n", - "2025-04-01 09:39:22,492 INFO Starting job - calculate_qe (a295270d-87db-41cc-aac2-88f7bd0b94d0)\n" + "2025-04-01 10:50:41,470 INFO Finished job - calculate_qe (8267d4c7-23ef-4ed3-9440-841790f572b7)\n", + "2025-04-01 10:50:41,471 INFO Starting job - generate_structures (381bd971-0704-48e7-9305-d01622a6847a)\n", + "2025-04-01 10:50:41,476 INFO Finished job - generate_structures (381bd971-0704-48e7-9305-d01622a6847a)\n", + "2025-04-01 10:50:41,477 INFO Starting job - get_dict (665d2bfd-ff33-4850-a685-935415c3a437)\n", + "2025-04-01 10:50:41,479 INFO Finished job - get_dict (665d2bfd-ff33-4850-a685-935415c3a437)\n", + "2025-04-01 10:50:41,479 INFO Starting job - get_dict (93373609-fa51-4b84-87ec-bf2bda04050e)\n", + "2025-04-01 10:50:41,481 INFO Finished job - get_dict (93373609-fa51-4b84-87ec-bf2bda04050e)\n", + "2025-04-01 10:50:41,481 INFO Starting job - get_dict (cd7bcaf9-8078-4f88-b006-e97607f75198)\n", + "2025-04-01 10:50:41,483 INFO Finished job - get_dict (cd7bcaf9-8078-4f88-b006-e97607f75198)\n", + "2025-04-01 10:50:41,483 INFO Starting job - get_dict (f9f7264c-9233-4f81-8d28-35309b2a030e)\n", + "2025-04-01 10:50:41,485 INFO Finished job - get_dict (f9f7264c-9233-4f81-8d28-35309b2a030e)\n", + "2025-04-01 10:50:41,485 INFO Starting job - get_dict (c4742465-a606-4c3e-8f9c-c5211c57c095)\n", + "2025-04-01 10:50:41,487 INFO Finished job - get_dict (c4742465-a606-4c3e-8f9c-c5211c57c095)\n", + "2025-04-01 10:50:41,488 INFO Starting job - calculate_qe (da2eec2b-cf50-42a3-9b3f-530ab34f6031)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[thinkpad:377581] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2025-04-01 09:39:32,629 INFO Finished job - calculate_qe (a295270d-87db-41cc-aac2-88f7bd0b94d0)\n", - "2025-04-01 09:39:32,631 INFO Starting job - calculate_qe (9b7f6e91-aa74-48f0-8364-e5d344d8a274)\n" + "2025-04-01 10:50:45,129 INFO Finished job - calculate_qe (da2eec2b-cf50-42a3-9b3f-530ab34f6031)\n", + "2025-04-01 10:50:45,130 INFO Starting job - calculate_qe (b1f556b4-d4f1-4c0a-9247-4ec834948138)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[thinkpad:377850] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2025-04-01 09:39:43,130 INFO Finished job - calculate_qe (9b7f6e91-aa74-48f0-8364-e5d344d8a274)\n", - "2025-04-01 09:39:43,133 INFO Starting job - calculate_qe (3427c80c-adb3-4cb5-a311-6a88af0d9aa7)\n" + "2025-04-01 10:50:49,010 INFO Finished job - calculate_qe (b1f556b4-d4f1-4c0a-9247-4ec834948138)\n", + "2025-04-01 10:50:49,011 INFO Starting job - calculate_qe (427e6966-a3c3-4fb9-8233-2635a5208a3b)\n" ] }, { "name": "stderr", "output_type": "stream", 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", 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By5Yteeihh9i0aRNxcXHMmTOHVatWla2j5Ovry8MPP8zzzz/PypUriY2N5U9/+hMAo0ePNuWz2tP43mEAfLLxCAXFJeaGERERqaFMHVm6HAsXLmTSpEn069cPq9XKqFGjePvtt8ueLyoqIjY2tmxEycXFhe+//55p06YxbNgwcnJyaNmyJQsWLGDIkCFl+7355ps4Ozszbtw4Tp8+TY8ePVizZg0BAQF2/4z2Nqh9EEG+7qRk5bN0RzKjujY1O5KIiEiN4xDrLNV0jrTO0h/9a+1B3lwRS/smvnw3qS8Wi8XsSCIiInZRq9ZZkupzZ/dQ3Jyt7E7MYsuRU2bHERERqXFUluq4el6ujLiqCaBFKkVERM5HZUmY0DcMgBV7UknMOG1uGBERkRpGZUmICPKlV/P6lNgMPoo+YnYcERGRGkVlSYDSRSoBPt2UwOlCLSMgIiJylsqSANAvshEh9TzIPF3E19sSzY4jIiJSY6gsCQBOVgv39goDYP6Gw2hFCRERkVIqS1JmdLcQPF2d2J+aw4a4k2bHERERqRFUlqSMn4cLt51ZxVvLCIiIiJRSWZJy7j1zv7gfY9I4cjLX3DAiIiI1gMqSlNMi0JvrWgdiGLBgg5YREBERUVmSc5xdRmDRlqPkFBSbG0ZERMRkKktyjmtbBdI80IvsgmK+3HLU7DgiIiKmUlmSc1itFsafOXdpQfQRbDYtIyAiInWXypKc16guTfFxd+bwiVx+3n/c7DgiIiKmUVmS8/Jyc+b2biEAfKBlBEREpA5TWZILurd3GBYLrDtwgoNp2WbHERERMYXKklxQSD1P+kc2AmBeVLy5YUREREyisiQVOruMwFe/JZKZV2RuGBEREROoLEmFejWvT0SQD6eLSvh8S4LZcUREROxOZUkqZLFYykaXFmw4QnGJzdxAIiIidqayJBd1y1VNCPB0ITHjNKv3pZodR0RExK5UluSi3F2cuLN7KKATvUVEpO5RWZJLMq5XM5ysFjYeTmdPUqbZcUREROxGZUkuSWM/Dwa3DwJgvkaXRESkDlFZkkt29kTvb3ckcTKnwNwwIiIidqKyJJesS2gAHZv6UVhs49NNWkZARETqBpUluWS/X0bgo1+PUKRlBEREpA5QWZLLcnOHYAJ93EjNKuD7XclmxxEREal2KktyWVydrYztoWUERESk7lBZkss2tkczXJ2sbD+awbaEU2bHERERqVYqS3LZAn3cGNqpMQDzN8SbG0ZERKSaqSxJpUzoHQ7Asp3JpGblm5xGRESk+qgsSaV0aOpHt2YBFNsMPv71iNlxREREqo3KklTahD6lo0ufbEwgv6jE5DQiIiLVQ2VJKm1gu0YE+7lzMreQ73YkmR1HRESkWqgsSaU5O1kZ1ysMKF1GwDAMcwOJiIhUA5UluSJ3XB2Cu4uVvclZbDqcbnYcERGRKqeyJFckwMuVkZ2bAFpGQEREaieVJbli488sI7BiTwrHTuWZnEZERKRqqSzJFWsT5EOflvWxGfBRtJYREBGR2kVlSarE2dGlTzclkFdYbHIaERGRqqOyJFXixoiGhNbzJCu/mK+3JZodR0REpMqoLEmVcLJauLd3GADztYyAiIhUkd8STvHNtkRT/66oLEmVGd2tKV6uThxIy2H9wRNmxxEREQeXX1TCk1/s4LHPt5t6xbXKklQZX3cXbuvaFChdpFJERORKzFkZy6ETuTTydePWzk1Ny6GyJFXq7FTcmpg0Dp/INTeMiIg4rC3x6fxv/WEAZt7aAT9PF9OyqCxJlWoe6M0NbQIBWKBFKkVEpBJOF5Yw5cudGAbc1rUpN0Y0MjWPypJUuQl9SpcR+HLrMbLzi0xOIyIijubNFbEcPpFLkK87M4a2NTuOypJUvWtaNaBFoBc5BcUs2nLM7DgiIuJANh1OZ96GM9Nvozrg52He9NtZKktS5SwWC+PPjC4tiI7HZtMyAiIicnF5hcU89eUODAPGdGvKDW0amh0JUFmSajKqSxN83Z05cjKPtbFpZscREREH8MbyWOJP5tHYz51na8D021kqS1ItPF2duaN7KKBlBERE5OJ+PXSybC2lWaM64utu/vTbWSpLUm3G9WyG1QLrD55gf2q22XFERKSGKp1+2wnAHVeHcF3rQJMTlaeyJNUmpJ4nN7UtvdzTzJVXRUSkZnv9hxgS0vMI9nPnmZsjzY5zDpUlqVZnlxH46rdjZOQVmpxGRERqmui4kyyIPgLA67d1xKcGTb+dpbIk1apHeD0iG/uSX2Tjs81HzY4jIiI1SG5BMVO+3AHAnd1DuaZVzZp+O0tlSaqVxWJhwplboHwUfYTiEpu5gUREpMaY9UMMx06dpom/R42cfjtLZUmq3fCrgqnn5UpixmlW7U01O46IiNQAGw6e4KNfS6ff3ritI95uziYnujCVJal27i5O3KVlBERE5IycgmKmnLn6bWyPUPq0bGByooqpLIld3N2zGc5WC5vi09mdmGl2HBERMdHM7/eRmHGapgEeTB9Sc6ffzlJZErsI8nNncIfGgJYREBGpy9YfOMHCjQlAzZ9+O0tlSexmQp8wAJZsT+JEToG5YURExO6y84uYurh0+u2eXs3o3aJmT7+d5TBlKT09nbFjx+Lr64u/vz8TJ04kJyenwn1SUlIYN24cQUFBeHl50aVLFxYvXlzuNfv37+eWW26hQYMG+Pr60rdvX9auXVudH6XO6hIaQKcQfwpLbHxy5l8VIiJSd7z2fQyJGacJqefB1EERZse5ZA5TlsaOHcuePXtYtWoVS5cu5ZdffuHBBx+scJ977rmH2NhYlixZwq5du7j11lsZM2YM27ZtK3vN0KFDKS4uZs2aNWzdupVOnToxdOhQUlJSqvsj1Un3nRld+ujXIxQWaxkBEZG64pf9x/l005npt1Gd8HKA6bezHKIs7du3j+XLl/O///2PHj160LdvX9555x0+++wzkpKSLrjfhg0bePTRR+nevTvNmzfn2Wefxd/fn61btwJw4sQJDhw4wLRp0+jYsSOtWrVi1qxZ5OXlsXv3bnt9vDplcPvGNPRx43h2AT/sTjY7joiI2EFWfhHTzky/je8dRq8W9U1OdHkcoixFR0fj7+9Pt27dyrb1798fq9XKxo0bL7hf7969+fzzz0lPT8dms/HZZ5+Rn5/P9ddfD0D9+vVp06YNH374Ibm5uRQXF/Puu+/SsGFDunbtWt0fq05ydbZyd89mAHygZQREROqEV5fuIykzn2b1PXlqUBuz41w2hxgDS0lJoWHDhuW2OTs7U69evQqny7744gtuv/126tevj7OzM56ennz99de0bNkSKF1devXq1YwYMQIfHx+sVisNGzZk+fLlBAQEXPB9CwoKKCj4/xOUs7KyrvAT1i139Qjln2sOsuNoBr8lnKJL6IWPtYiIOLafYtP4fEvp7a7eGNURT1eHqB7lmDqyNG3aNCwWS4WPmJiYSr//jBkzyMjIYPXq1WzZsoUnnniCMWPGsGvXLgAMw+DPf/4zDRs2ZN26dWzatIkRI0YwbNgwkpMvPEU0c+ZM/Pz8yh4hISGVzlgXNfB2Y/hVwYAWqRQRqc0yTxcxbXHp39wJfcLo0dyxpt/OshiGYZj1zY8fP87JkycrfE3z5s35+OOPmTx5MqdOnSrbXlxcjLu7O4sWLWLkyJHn7BcXF0fLli3ZvXs37dq1K9vev39/WrZsydy5c/nxxx8ZMGAAp06dwtfXt+w1rVq1YuLEiUybNu28mc43shQSEkJmZma595EL252YydB31uNstbB+6o0E+bmbHUlERKrYlEU7WLT1GGH1Pfnhr9fi4epkdqRysrKy8PPzu+jfb1PHwgIDAwkMvPgdhnv16kVGRgZbt24tO5dozZo12Gw2evTocd598vLyALBayw+eOTk5YbPZKnyN1Wote835uLm54ebmdtHccmHtm/jRPawem+LT+fjXIzw50PHmsEVE5MLWxqSxaOsxLBZ4c3SnGleULodDnOAdGRnJoEGDeOCBB9i0aRNRUVFMmjSJO+64g+Dg0umcxMREIiIi2LRpEwARERG0bNmShx56iE2bNhEXF8ecOXNYtWoVI0aMAEpLWEBAAPfeey87duxg//79TJkyhcOHD3PzzTeb9XHrjLOLVH6yKYH8ohJzw4iISJXJzCti2lelV7/d1yecq8PqmZzoyjhEWQJYuHAhERER9OvXjyFDhtC3b1/ee++9sueLioqIjY0tGy1ycXHh+++/JzAwkGHDhtGxY0c+/PBDFixYwJAhQwBo0KABy5cvJycnhxtvvJFu3bqxfv16vv32Wzp16mTK56xLbmrbiCb+HqTnFrJk+4WXgBAREcfy0tK9pGYVEN7AiycHOP7MgannLNUWlzrnKeea+3Mcs36IIbKxL9//pS8Wi8XsSCIicgV+3JfKxAVbsFjgy4d70bVZzR1VutS/3w4zsiS10x1Xh+DuYmVfchYbD6ebHUdERK5AZl4R078qvfrt/r7hNbooXQ6VJTGVv6crt3ZpCsC8qMMmpxERkSvx4nd7SMsuoHmgF5NrwfTbWSpLYroJvcMAWLU3laPpeeaGERGRSlm1N5WvtiVitcDs0Z1wd3Hcq9/+SGVJTNeqkQ99WzbAZpTeYFdERBxLRl4hT39dOv32wDXNa92dGVSWpEY4u4zAZ5sSyCssNjeMiIhclheW7OF4dgEtAr14/KbWZsepcipLUiPc0KYhYfU9ycovZvFviWbHERGRS7RiTwrfbE/CaoE5Y66qVdNvZ6ksSY1gtVq498y5S/OjDmOzaUULEZGa7lRuIc98vRuAh65rwVUh/uYGqiYqS1Jj3Na1Kd5uzsQdz2X9wRNmxxERkYt4fskeTuQU0KqhN4/1b2V2nGqjsiQ1ho+7C7d11TICIiKOYPnuZJbsSMLJamH26E64Ode+6bezVJakRhnfOwyLBdbGHufQ8Ryz44iIyHmk5xby7Del028PX9ecTrV0+u0slSWpUcIaeHFjm4YALNgQb24YERE5r+e+3c2JnELaNPLhL/1q7/TbWSpLUuOMP7OMwJdbj5GVX2RuGBERKef7Xcks3ZlcJ6bfzlJZkhqnb8sGtGroTW5hCYu2HDM7joiInHEip6Bs+u2R61vQoamfyYnso1JlKTc3t6pziJSxWCxlo0sLNsRTomUERERqhOe+3U16biERQT48emPtn347q1JlqVGjRtx3332sX7++qvOIADCycxP8PFxISM9jTUya2XFEROq8pTuT+H5XCs5npt9cnevO5FSlPunHH39Meno6N954I61bt2bWrFkkJSVVdTapwzxdnbnj6hAA5m/QMgIiImY6nl3AjLPTbze0pH2TujH9dlalytKIESP45ptvSExM5OGHH+aTTz6hWbNmDB06lK+++oriYt3bS67cuF7NsFog6uBJYlOyzY4jIlInGYbBjG92cyqviMjGvky6oaXZkezuisbQAgMDeeKJJ9i5cyd/+9vfWL16NbfddhvBwcE899xz5OXlVVVOqYOaBngysF0QoNElERGzfLczmeV7zk6/daxT029nXdEnTk1N5Y033qBt27ZMmzaN2267jR9//JE5c+bw1VdfMWLEiCqKKXXVhD7hAHz1WyKncgtNTiMiUrekZefz3Lel02+TbmxJu+C6Nf12lnNldvrqq6+YN28eK1asoG3btjzyyCPcfffd+Pv7l72md+/eREZGVlVOqaOuDgugbWNf9iZn8dnmo/zp+hZmRxIRqRMMw+DZr3eTkVdE28a+/LkOTr+dVamRpQkTJhAcHExUVBTbt29n0qRJ5YoSQHBwMM8880xVZJQ6zGKxMOHMMgIfRcdTXGIzN5CISB2xZEcSK/em4uJkYc6YTrg41b3pt7MqNbKUnJyMp6dnha/x8PDg+eefr1Qokd8b1imYWT/EkJSZz4o9qdzcsbHZkUREarW0rHye+3YPAI/e2IrIxr4mJzJXpWpicXExWVlZ5zyys7MpLNR5JVK13F2cGNsjFIB5UTrRW0SkOhmGwdNf7yLzdBHtm/jq9AcqWZb8/f0JCAg45+Hv74+HhwfNmjXj+eefx2bTlIlUjbE9m+FstbDlyCl2Hcs0O46ISK319bZEVu9Lw8WpdPHJujz9dlaljsD8+fMJDg7m6aef5ptvvuGbb77h6aefpkmTJvznP//hwQcf5O2332bWrFlVnVfqqEa+7mXTb/O0jICISLVIzcrnhSWl02+P9W9NRFDdnn47q1LnLC1YsIA5c+YwZsyYsm3Dhg2jQ4cOvPvuu/z444+Ehoby6quv8vTTT1dZWKnbJvQJ59vtSSzdkcz0wZEE+riZHUlEpNYwDIOnv9pFVn4xHZv68dC1zc2OVGNUamRpw4YNdO7c+ZztnTt3Jjo6GoC+ffuSkJBwZelEfueqEH86h/pTWGJj4cYjZscREalVFv+WyI8xabg6WZk9uhPOmn4rU6kjERISwvvvv3/O9vfff5+QkNL7eZ08eZKAgIArSyfyB+N7hwHw8a8JFBbrnDgRkaqQkpnPi9+dmX67qRWtG/mYnKhmqdQ03OzZsxk9ejQ//PADV199NQBbtmwhJiaGL7/8EoDNmzdz++23V11SEWBIh8a89v0+UrMKWLYriZGdm5odSUTEoRmGwfSvdpKdX0ynEH8evEbTb39UqZGl4cOHExsby5AhQ0hPTyc9PZ3BgwcTExPD0KFDAfjTn/7E3/72tyoNK+LiZGVcz2YAzIuKxzAMkxOJiDi2RVuPsTb2eOn0220dNf12Hpc9slRUVMSgQYOYO3cuM2fOrI5MIhW6s3sob685yM5jmfyWcIquzeqZHUlExCElZ57m5e/2AvDEgNa00vTbeV12fXRxcWHnzp3VkUXkktT3duOWTsFA6eiSiIhcPsMwmLp4F9kFxVwV4s8Dmn67oEqNtd19993nPcFbxF4m9AkH4IfdKSRnnjY5jYiI4/liy1F+2X8cV+fSq9+crBazI9VYlTrBu7i4mA8++IDVq1fTtWtXvLy8yj2vc5WkurUN9qVHeD02Hk7no+gjPDUowuxIIiIOIzHjNK8s3QfAkwNa07Kht8mJarZKlaXdu3fTpUsXAPbv31/uOYtFzVTsY0KfcDYeTufTTQn8pV8r3F2czI4kIlLjGYbBtMU7yS4opkuoPxP7avrtYipVltauXVvVOUQu201tG9HE34PEjNN8uz2R268ONTuSiEiN99nmo6w7cAI3ZytvavrtklzR9YEHDx5kxYoVnD5des6ILuMWe3KyWri3t5YREBG5VMdO5fHK0tKr36YMbEOLQE2/XYpKlaWTJ0/Sr18/WrduzZAhQ0hOTgZg4sSJTJ48uUoDilTk9m6heLg4EZOSTfShk2bHERGpsUqn33aRW1hCt2YBZRfKyMVVqiw9/vjjuLi4kJCQgKenZ9n222+/neXLl1dZOJGL8fN0YVTXJoCWERARqcgnmxJYf/AE7i6afrtclSpLK1eu5PXXX6dp0/K3mmjVqhVHjugGp2JfZ+8Xt3pfKkfT88wNIyJSAx1Nz+O1ZaVXv00ZGEF4A6+L7CG/V6mylJubW25E6az09HTc3NyuOJTI5WjZ0IdrWjXAMGDBhniz44iI1Cg2m8HUxTvJLSyhe1g9Jpz5B6ZcukqVpWuuuYYPP/yw7GuLxYLNZuONN97ghhtuqLJwIpfqvjNz759vOUpuQbHJaUREao6FmxLYEHcSdxcrb9zWEaum3y5bpZYOeOONN+jXrx9btmyhsLCQp556ij179pCenk5UVFRVZxS5qOtaBxLewIvDJ3JZ/Nsx7ukVZnYkERHTHU3PY+b3pdNvUwdFEKbpt0qp1MhS+/bt2b9/P3379uWWW24hNzeXW2+9lW3bttGiRYuqzihyUVarhXt7lS4jMH9DPDablhEQkbrNZjOY8uUO8gpL6B5ej3v1j8hKq9TIEoCfnx/PPPNMVWYRuSK3dQthzsr9HDqeyy8HjnN9m4ZmRxIRMc1Hvx7h10PpeLg4Mfu2Tpp+uwKVLksZGRls2rSJtLQ0bDZbuefuueeeKw4mcrm83ZwZ3S2ED6IOMy8qXmVJROqsIydzmfVDDADTh0QQWv/ci7Lk0lWqLH333XeMHTuWnJwcfH19y90PzmKxqCyJae7t3Yx5Gw7z8/7jHEzL0c0hRaTOKZ1+28npohJ6Nq/H3T2amR3J4VXqnKXJkydz3333kZOTQ0ZGBqdOnSp7pKenV3VGkUvWrL4X/SJKR5Q+jI43N4yIiAkWRMez6XA6nq5OvKnptypRqbKUmJjIX/7yl/OutSRitrNL+H+59RiZp4tMTiMiYj/xJ3J5ffnZ6bdIQurp73RVqFRZGjhwIFu2bKnqLCJVoneL+rRp5ENeYQmLthw1O46IiF2cvfotv8hG7xb1Gds91OxItUalzlm6+eabmTJlCnv37qVDhw64uLiUe3748OFVEk6kMiwWC+P7hDH9q13M3xDPhD7hugeSiNR68zbEszn+FF6uTrw+SotPViWLYRiXvSCN1XrhASmLxUJJSckVhXI0WVlZ+Pn5kZmZia+vr9lxBDhdWEKvWT+SkVfEe+O6MqBdkNmRRESqzaHjOQx5ex35RTZeHdmesTqp+5Jc6t/vSk3D2Wy2Cz7qWlGSmsnD1Yk7ri4dgp4XFW9uGBGRalRy5uq3/CIbfVs24C5Nv1W5yypLQ4YMITMzs+zrWbNmkZGRUfb1yZMnadu2bZWFE7kS9/RqhpPVQvShk+xLzjI7johItZgXdZitR07h7ebMrFEdyi3nI1XjssrSihUrKCgoKPv6tddeK7dUQHFxMbGxsVWXTuQKBPt7MOjM9Nt8jS6JSC0UdzyHN1eU/t195uZImgbo6rfqcFll6Y+nN1XidCcRuxrfJwyAb7Ynkp5baG4YEZEqVGIzmLJoBwXFNq5p1YA7rg4xO1KtValzlkQcRbdmAbRv4ktBsY1PNyWYHUdEpMq8v/4QvyVk4OPmzOujOmr6rRpdVlmyWCzn/J+h/3OkJrNYLEzoXbpI5UfRRygqsV1kDxGRmu9gWjazV+4H4NmhkQT7e5icqHa7rHWWDMNg/PjxuLm5AZCfn8/DDz+Ml5cXQLnzmURqiqGdGjPzh32kZOWzfHcKwzoFmx1JRKTSSmwGTy7aSWGxjetaBzKmm6bfqttllaV777233Nd33333Oa/RTXSlpnFzduKuHs14+8cDzN8Qr7IkIg7tv+sOsf1oBj7uuvrNXi6rLM2bN6+6cohUq7t7hvKfnw6y9cgpdh7LoGNTf7MjiYhctgOp2fztzPTbjKFtaeyn6Td70AneUic09HFnaMfSESUtUikijqi4xMaTi3ZQWGLjhjaBjO7a1OxIdYbKktQZ43uHAbB0ZxJpWfnmhhERuUzv/nKIHccy8XF3ZuatuvrNnhymLKWnpzN27Fh8fX3x9/dn4sSJ5OTkVLhPXFwcI0eOJDAwEF9fX8aMGUNqauoVv684pk4h/nQJ9aeoxGDhRi0jICKOIzYlm3+sPgDAC8PaEeTnbnKiusVhytLYsWPZs2cPq1atYunSpfzyyy88+OCDF3x9bm4uAwYMwGKxsGbNGqKioigsLGTYsGHYbLZKv684tgl9SpcRWLjxCAXFuo+hiNR8Rb+bfusX0ZBbuzQxO1KdYzEcYBnuffv20bZtWzZv3ky3bt0AWL58OUOGDOHYsWMEB597ddPKlSsZPHgwp06dKruTcGZmJgEBAaxcuZL+/ftX6n3P51LvWizmKyqxcc3ra0nJymfO6E6M0py/iNRw/1xzgNkr9+Pr7syqJ66jka9GlarKpf79doiRpejoaPz9/csKDUD//v2xWq1s3LjxvPsUFBRgsVjK1oQCcHd3x2q1sn79+kq/rzg2Fycr43o1A2DehsO6ZY+I1GgxKVn848fS6bcXb2mnomQShyhLKSkpNGzYsNw2Z2dn6tWrR0pKynn36dmzJ15eXkydOpW8vDxyc3N58sknKSkpITk5udLvC6VFLCsrq9xDHMed3UNxc7ayOzGLrUdOmR1HROS8ikpsTP5iB0UlBv0jGzHiKk2/mcXUsjRt2rSyW6hc6BETE1Op9w4MDGTRokV89913eHt74+fnR0ZGBl26dMFqvbKPPXPmTPz8/MoeISFaPdWR1PNyLfulo2UERKSm+s9PcexJysLf04XXbm2vq99MdFmLUla1yZMnM378+Apf07x5c4KCgkhLSyu3vbi4mPT0dIKCgi6474ABA4iLi+PEiRM4Ozvj7+9PUFAQzZs3B6j0+06fPp0nnnii7OusrCwVJgczoW8Yn285yvI9KSRlnNZ9lUSkRtmblMU7a85Mvw1vR0MfTb+ZydSyFBgYSGBg4EVf16tXLzIyMti6dStdu3YFYM2aNdhsNnr06HHR/Rs0aFC2T1paGsOHD7+i93Vzcyt3LpQ4noggX3o1r0/0oZN8GH2EaYMjzI4kIgL8/9VvRSUGA9o2Yrhu0WQ6hzhnKTIykkGDBvHAAw+wadMmoqKimDRpEnfccUfZFWuJiYlERESwadOmsv3mzZvHr7/+SlxcHB9//DGjR4/m8ccfp02bNpf8vlJ7TegTBsBnmxM4XahlBESkZvjX2oPsTc4iwNOFV0fq3m81gUOUJYCFCxcSERFBv379GDJkCH379uW9994re76oqIjY2Fjy8vLKtsXGxjJixAgiIyN56aWXeOaZZ5g9e/Zlva/UXv0iGxFSz4OMvCK+2Z5odhwREfYkZfLPNQcBePGW9gT6aBajJnCIdZZqOq2z5Lj+t+4QryzbR+tG3qx47Fr9C05ETFNYbGP4P9cTk5LNoHZB/OfuLvqdVM1q1TpLItVldLcQPF2d2J+aw4a4k2bHEZE67J9rDxKTkk09L1deGamr32oSlSWp0/w8XLjtzCreWkZARMyyOzGTf60tnX576ZZ2NPDW9FtNorIkdd69vcMA+DEmlSMnc80NIyJ1TmFx6dVvJTaDmzs0ZmhHXWBU06gsSZ3XItCb61oHYhiwYMMRs+OISB3zzpoDxKRkU9/LlZduaWd2HDkPlSUR/n8ZgUVbjpJTUGxuGBGpM3Yey+DfP8UB8PKI9tTX9FuNpLIkAlzbKpDmgV5kFxSzeOsxs+OISB1QUFxSNv02tGNjhnRobHYkuQCVJRHAarUw/sy5S/M3xGOzaUUNEale/1h9gP2pOTTwduWlW9qbHUcqoLIkcsaoLk3xcXfm8Ilcft5/3Ow4IlKL7TiawdyfS6ffXhnRgXperiYnkoqoLImc4eXmzO3dSm+I/EHUYZPTiEhtlV9UOv1mM2B4p2AGtb/wjdulZlBZEvmde3uHYbHAugMnOJiWbXYcEamF3lp9gANpOTTwduPF4br6zRGoLIn8Tkg9T/pHNgJKz10SEalK2xJO8d4vpdNvr41sT4Cm3xyCypLIH5xdRmDx1kQy84rMDSMitcbvp99GXBXMgHaafnMUKksif9CreX0ignw4XVTC51sSzI4jIrXE31ftJ+54LoE+bryg6TeHorIk8gcWi6VsdGnBhiOUaBkBEblCW4+c4r/rDgEwc2QH/D01/eZIVJZEzuOWq5oQ4OlCYsZpVu1NNTuOiDiw/KISppyZfru1cxP6t21kdiS5TCpLIufh7uLEnd1DAZinZQRE5ArMWRnLoRO5NPRx4/lhmn5zRCpLIhcwrlcznKwWNh5OZ09SptlxRMQBbT2Szv/Wl/6Da9aoDvh5upicSCpDZUnkAhr7eTD4zGJxC7SMgIhcptOFJTy5aCeGUXqHgBsjNP3mqFSWRCpw9kTvb7YncTKnwNwwIuJQZq+M5fCJXBr5uvHcsLZmx5EroLIkUoEuoQF0bOpHYbGNTzdpGQERuTSbDqeX3TZp1qiO+Hlo+s2RqSyJVOD3ywh89OsRikps5gYSkRrvdGEJT325A8OAMd2ackObhmZHkiuksiRyETd3CCbQx43UrAJ+2J1idhwRqeHeWBFD/Mk8Gvu58+xQTb/VBipLIhfh6mxlbA8tIyAiF7fx0EnmRcUDpdNvvu6afqsNVJZELsHYHs1wdbKyLSGD7UczzI4jIjVQXmExU77cCcAdV4dwXetAkxNJVVFZErkEgT5uDO3UGID5Gl0SkfN4Y3ksCel5BPu588zNkWbHkSqksiRyiSb0Dgdg2a5k0rLyTU4jIjVJdNxJ5p9Zj+312zrio+m3WkVlSeQSdWjqR7dmARSVGHz86xGz44hIDZFbUMxTi3cAcGf3UK5ppem32kZlSeQyTOhTOrq0cGMC+UUlJqcRkZpg1g8xHE0/TRN/D54eEmF2HKkGKksil2Fgu0YE+7lzMreQpTuTzY4jIibbcPAEH50ZaX59lKbfaiuVJZHL4OxkZVyvMKB0GQHDMMwNJCKmySko5qnFpVe/je0RSt9WDUxOJNVFZUnkMt1xdQjuLlb2JGWxOf6U2XFExCQzv9/HsVOnaRrgwfQhuvqtNlNZErlMAV6ujOzcBNAilSJ11foDJ1i4sfR+kW/c1hFvN2eTE0l1UlkSqYTxZ5YRWLEnhcSM0yanERF7ys4vYuqZ6bdxPZvRu4Wm32o7lSWRSmgT5EOflvWxGfBhdLzZcUTEjl77PobEjNOE1PNg2mBd/VYXqCyJVNLZ0aXPNh0lr7DY5DQiYg+/7D/Op5vOTL+N6oSXpt/qBJUlkUq6MaIhofU8yTxdxNfbEs2OIyLVLCu/iGlnpt/u7dWMXi3qm5xI7EVlSaSSnKwW7u0dBsD8qHgtIyBSy722bB9JmfmE1vNkqqbf6hSVJZErMLpbU7xcnTiQlkPUwZNmxxGRavJTbBqfbT4KwJu3dcTTVdNvdYnKksgV8HV34bauTQEtIyBSW2XlFzH9q10ATOgTRo/mmn6ra1SWRK7Q2am4NbFpxJ/INTeMiFS5V5buJTkzn7D6njw1UNNvdZHKksgVah7ozQ1tAjEMWKBlBERqlbUxaXyx5RgWC7w5uhMerk5mRxITqCyJVIEJfUqXEVi05RjZ+UUmpxGRqpB5uohpX5Ve/XZfn3CuDqtnciIxi8qSSBW4plUDWgR6kVNQzJdbj5kdR0SqwMtL95KaVUB4Ay+eHNDG7DhiIpUlkSpgsVgYf2Z0acGGeGw2LSMg4sjWxKTy5dbS6bfZoztq+q2OU1kSqSKjujTB192Z+JN5/LQ/zew4IlJJmXlFTFtcevXb/X3D6dpM0291ncqSSBXxdHXmju6hAMyLijc3jIhU2ovf7SEtu4DmgV5M1vSboLIkUqXG9WyG1QLrDpzgQGq22XFE5DKt3pvKV9sSsVpg9uhOuLto+k1UlkSqVEg9T25q2wiAeRvizQ0jIpclI6+Q6V+XTr89cE1zuoQGmJxIagqVJZEqdnYZga9+O0ZmnpYREHEULyzZw/HsAloEevH4Ta3NjiM1iMqSSBXrEV6PyMa+5BfZ+GxzgtlxROQSrNiTwjfbkzT9JuelsiRSxSwWCxPO3ALlw+gjFJfYzA0kIhU6lVvIM1/vBuDBa1vQWdNv8gcqSyLVYPhVwdTzciUx4zSr9qaaHUdEKvD8kj2cyCmgVUNvHuvfyuw4UgOpLIlUA3cXJ+46s4zA/9Yf1uiSSA21fHcyS3Yk4WS1aPpNLkhlSaSajOvVDGerha1HTjFqbjQH07SUgEhNkp5byLPflE6/PXRtczqF+JsbSGoslSWRatLI151/3NEZH3dndhzNYMjb63n35zhKdCsUkRrhuW93cyKnkNaNvPmrpt+kAipLItXo5o6NWfX4ddzQJpDCYhszf4jhtrkbiDueY3Y0kTrt+13JLN2ZjJPVwpzRV+HmrOk3uTCVJZFqFuTnzgfjr+aN2zri4+bMtoQMhvxjHf/95ZBGmURMcDKngBlnpt8eub4FHZr6mZxIajqVJRE7sFgsjOkWwsonruW61oEUFNt49ft9jHk3mkMaZRKxq+e+3cPJ3EIignx49EZNv8nFqSyJ2FFjPw/mT7ia10d1wMfNma1HTjH4H+v43zqNMonYw9KdSSzblVx29Zurs/4MysXpp0TEziwWC7dfHcqKx6/lmlYNKCi28cqyfdz+bjSHT+SaHU+k1jqRU8Bz3+4B4M83tKR9E02/yaVRWRIxSbC/Bx/e152Zt3bA282ZLUdOMfgfv/DB+sPYNMokUqUMw2DGN7tJzy0ksrEvk25oaXYkcSAqSyImslgs3Nm9dJSpb8sG5BfZeGnpXu5471fiNcokUmW+25nMD7tTcLZamD26o6bf5LLop0WkBmji78FHE7vz6sj2eLk6sSk+nUH/+IX5URplErlSadn5PPdt6dVvk25sSbtgTb/J5XGYspSens7YsWPx9fXF39+fiRMnkpNT8VVEcXFxjBw5ksDAQHx9fRkzZgypqf9/n674+HgmTpxIeHg4Hh4etGjRgueff57CwsLq/jgi57BYLIzt0Yzlj11L7xb1yS+y8cJ3e7nzv7+ScDLP7HgiDskwDJ79ejcZeUW0bezLnzX9JpXgMGVp7Nix7Nmzh1WrVrF06VJ++eUXHnzwwQu+Pjc3lwEDBmCxWFizZg1RUVEUFhYybNgwbLbS+3TFxMRgs9l499132bNnD3//+9+ZO3cuTz/9tL0+lsg5Qup58vHEHrw8oj2erk5sPJzOwLd+YcGGeI0yiVyG7PwiXlm2j5V7U3FxKr36zcXJYf7sSQ1iMQyjxv/23bdvH23btmXz5s1069YNgOXLlzNkyBCOHTtGcHDwOfusXLmSwYMHc+rUKXx9fQHIzMwkICCAlStX0r9///N+rzfffJP//Oc/HDp06JLzZWVl4efnR2ZmZtn3EqkKR9PzeOrLnUQfOglAz+b1ePO2ToTU8zQ5mUjNVVxi4/MtR/nbyv2czC2dKXhqUBseuV6jSlLepf79doiKHR0djb+/f1lRAujfvz9Wq5WNGzeed5+CggIsFgtubm5l29zd3bFaraxfv/6C3yszM5N69epVXXiRKxBSz5OF9/fg5Vva4eHixK+HSkeZPorWKJPI+aw7cJyb317PM1/v5mRuIc0DvXj/3m786boWZkcTB+YQZSklJYWGDRuW2+bs7Ey9evVISUk57z49e/bEy8uLqVOnkpeXR25uLk8++SQlJSUkJyefd5+DBw/yzjvv8NBDD1WYp6CggKysrHIPkepitVoY1yuMFY9dS4/weuQVljDj2z3c/f5GjqbrXCYRgINpOUyYt4lx728iNjUbPw8Xnh/WlhWPXUu/yEZYLBazI4oDM7UsTZs2DYvFUuEjJiamUu8dGBjIokWL+O677/D29sbPz4+MjAy6dOmC1Xrux05MTGTQoEGMHj2aBx54oML3njlzJn5+fmWPkJCQSmUUuRyh9T359IGevDi8dJRpQ9xJBr31Cx//egQHmE0XqRancgt5/tvdDHzrF9bGHsfZamFCnzB+nnI9E/qE6xwlqRKmnrN0/PhxTp48WeFrmjdvzscff8zkyZM5depU2fbi4mLc3d1ZtGgRI0eOrPA9Tpw4gbOzM/7+/gQFBTF58mSmTJlS9nxSUhLXX389PXv2ZP78+ectU79XUFBAQUFB2ddZWVmEhITonCWxmyMnc5myaCeb4tMB6NuyAbNGdaBpgM5lkrqhsNjGh9HxvP3jAbLyiwHoH9mQ6UMiaRHobXI6cRSXes6SQ53gvWXLFrp27QqUnsA9aNCgC57gfT5r1qyhf//+7Nu3jzZt2gClI0o33HADXbt25eOPP8bJyemy8+kEbzGDzWawIDqe15fHkF9kw9vNmaeHRHJn9xBNOUitZRgGq/amMvOHmLLbA0UE+TBjaFv6tGxgcjpxNLWqLAEMHjyY1NRU5s6dS1FRERMmTKBbt2588sknQGnp6devHx9++CHdu3cHYN68eURGRhIYGEh0dDR//etfGT9+PHPmzCnb5/rrr6dZs2YsWLCgXFEKCgq65GwqS2KmwydyeerLHWyOLx15vaZVA2aN6kgTfw+Tk4lUrb1JWbyybC8b4kpnJBp4u/HkgNaM7haCk1X/QJDLd6l/v53tmOmKLFy4kEmTJtGvXz+sViujRo3i7bffLnu+qKiI2NhY8vL+/4TX2NhYpk+fTnp6OmFhYTzzzDM8/vjjZc+vWrWKgwcPcvDgQZo2bVru+zlIhxQhvIEXnz3Yi/kb4nljeQzrDpxg4N9/4dmbI7n9ao0yieNLy85nzor9fLH1KIYBrs5W7u8bziM3tMTbzWH+jIkDc5iRpZpMI0tSUxw6nsOUL3ey9UjpKNO1rQOZdWsHgjXKJA4ov6iE99cf5t9rD5JbWALA0I6NmTooQmuNSZWoddNwNZnKktQkJTaDeVGHeXNFLAXFNnzcnJkxtC2juzXVKJM4BMMw+G5nMq//EENixmkAOoX489zQSLo20zp4UnVUluxIZUlqorjjOTy5aAfbEjIAuL5NIDNv7UBjP40ySc21LeEULy/dy29nfm4b+7kzdVAEwzsFY9V5SVLFVJbsSGVJaqoSm8H76w8xe+V+Cott+Lg789zQttzWVaNMUrMkZpzmjeUxfLs9CQAPFyf+dH0LHrimOR6ul3+VssilUFmyI5UlqekOpmXz5KKdbD+aAcCNEQ15bWQHgvzczQ0mdV5uQTFzf47jvV8OUVBsw2KBUV2aMmVgGxr56udTqpfKkh2pLIkjKC6x8b/1h/nbqtJRJl93Z54b1o5RXZpolEnszmYz+PK3Y8xeEUtadukiv93D6/Hc0La0b+JncjqpK1SW7EhlSRzJgdRsnly0gx3HMgHoF9GQ127toH/Fi91Ex53klWV72ZNUel/N0HqePD0kgoHtglTcxa5UluxIZUkcTXGJjffWHeKtVQcoLCkdZXpheDtGdtYok1Sf+BO5vPb9PlbuTQXAx82ZR/u15N7eYbg567wksT+VJTtSWRJHtT81m8lf7GBXYukoU//IRrx2a3sa+miUSapO5uki3vnxAAui4ykqMbBa4K4eoTzevzX1vd3Mjid1mMqSHaksiSMrLrHx7i+HeGv1fopKDPw8XHhxeDtuuSpYo0xyRYpLbHyyKYG/r9rPqbwiAK5rHcgzN0fSupGPyelEVJbsSmVJaoOYlCyeXLSD3Yml55EMaNuIV0d2INBH//KXy7c2No1Xl+3jYFoOAK0aevPMzZFc36ahyclE/p/Kkh2pLEltUVRiY+5Pcby95gBFJQb+nqWjTMM7aZRJLs3+1GxeWbaPX/YfByDA04UnbmrNnd1DcXaympxOpDyVJTtSWZLaZl9yFpO/2MHe5NJRpkHtgnh5RHuNMskFncwp4O+r9/PppqOU2AxcnCyM7x3GpBtb4efhYnY8kfNSWbIjlSWpjYpKbPx7bRzvrDlAsc0gwNOFl0e0Z2jHYLOjSQ1SUFzCgg3xvLPmINn5xQAMbNeI6YMjCWvgZXI6kYqpLNmRypLUZnuSMnly0U72nRllGtIhiJduaU8DXcVUpxmGwfLdKcz8IYaE9DwA2gX78uzNbenVor7J6UQujcqSHaksSW1XWGzjX2sP8q+1Bym2GdTzcuXlW9pzc8fGZkcTE+w6lsnLy/ay6XA6AA193JgysA23dmmKk252Kw5EZcmOVJakrtidmMmTi3YQk5INwM0dG/PS8HZaK6eOSM3K543lsXy17RiGAW7OVh66tjkPXdcCLzdns+OJXDaVJTtSWZK6pLDYxj/XHOBfP8VRYjOo7+XKKyPaM7iDRplqq9OFJbz3yyHm/hzH6aISAEZcFcxTgyII9vcwOZ1I5aks2ZHKktRFu46VjjLFppaOMg3rFMyLw9tRz8vV5GRSVWw2g293JPLG8liSM/MB6BLqz4yhbekcGmByOpErp7JkRypLUlcVFJfwzo8H+c/PpaNMDbxdeWVEBwa1DzI7mlyhrUfSeWnpPnYczQCgib8H0wZHMLRjY625JbWGypIdqSxJXbfzWAZPLtrB/tTS1ZqHnxllCtAok8M5mp7HrOUxLNuZDICXqxOP3NCSiX3DcXfRzW6ldlFZsiOVJZHSUaZ/rD7A3J/jsBnQwNuN10a2Z0A7jTI5guz8Iv79Uxzvrz9MYbENiwVu7xbCEwNa68bKUmupLNmRypLI/9t+tHSU6ew9wUZcFcwLw9vh76lRppqoxGbwxZajzFkZy4mcQgB6t6jPsze3pW2wfp9J7aayZEcqSyLl5ReV8NbqA7z3S+koU6CPGzNHdqB/20ZmR5PfiTp4gpeX7i1bCiK8gRdPD4mkf2RDnZckdYLKkh2pLImc37aEUzy5aAdxx3MBuLVzE54f1g4/T90rzEyHjufw2vf7WL0vDQBfd2f+2r8143o2w9VZN7uVukNlyY5UlkQuLL+ohL+v2s9/1x3CZkAjXzdm3tqBGyM0ymRvGXmF/OPHA3wUfYRim4GT1cK4ns34a79WOhlf6iSVJTtSWRK5uK1HTjFl0Q4OnSgdZRrVpSnPDWurO9LbQVGJjY9/PcJbqw+QeboIgBsjGvL0kEhaNvQ2OZ2IeVSW7EhlSeTS5BeVMGdlLP9bfxjjzCjTrFs7ckNEQ7Oj1UqGYfDjvjRe+35fWUlt08iHZ4dGck2rQJPTiZhPZcmOVJZELs+W+HSmfLmTw2f+gI/u2pRnh2qUqSrtS87ilWV7iTp4EoAG3q48cVMbxnRrirOTzksSAZUlu1JZErl8pwtLmL0ylg+iSkeZgnzdmTWqA9e30SjTlTieXcDfVsXy+eaj2AxwdbJyX99w/nxDC3zcVUZFfk9lyY5UlkQqb3N8OlMW7SD+ZB5QuhDiM0Mj8dUf9suSX1TCB1GH+ffaOHIKigG4uUNjpg2OIKSep8npRGomlSU7UlkSuTKnC0t4Y0UM8zfEYxjQ2M+d10d15NrWOq/mYgzDYNmuZGb9EMOxU6cB6NjUjxlD23J1WD2T04nUbCpLdqSyJFI1Nh1OZ8qXOzhyZpTpzu4hPD0kUtNHF7DjaAYvL93LliOngNKpzKcGtWHEVU2wWrWopMjFqCzZkcqSSNXJKyzmjeWxzN8QD5Te7X7WqA66eut3kjNP88byWL7elgiAh4sTD13XnAevbY6nq7PJ6UQch8qSHaksiVS9Xw+d5Kkvd5KQXjrKdFePUJ4eEom3W90tA3mFxcz9+RDv/RJHfpENKF2vasrANgT56Wa3IpdLZcmOVJZEqkduQTGvL4/hw+gjQOko0xu3daRPywYmJ7Mvm81g8W/HeHNFLGnZBQB0D6vHjKFt6dDUz+R0Io5LZcmOVJZEqteGuBM89eXOshOY7+4ZyvTBkXjVgVGmjYdO8vKyvexOzAIgpJ4HTw+OZFD7IN3sVuQKqSzZkcqSSPXLLShm5g/7+PjXBACaBpSOMvVuUTtHmY6czGXm9zEs35MCgI+bM5NubMn4PmG4OTuZnE6kdlBZsiOVJRH72XDwBFO+3EliRuko0z29mjF1UEStGWXKyi/in2sOMj8qnsISG1YL3Nk9lMdvak0Dbzez44nUKipLdqSyJGJfOQXFvPb9Pj7ZWDrKFFLPgzdv60TP5vVNTlZ5xSU2Pt18lL+v2k96biEA17RqwLM3t6VNkI/J6URqJ5UlO1JZEjHH+gMnmLr4/0eZ7u3VjKmDIxzu8vmf9x/n1WV72Z+aA0CLQC+evbkt17cJ1HlJItVIZcmOVJZEzJOdX8Rr38fw6abSUabQep68eVtHejjAKNPBtGxeWbaPn2KPA+Dv6cLj/VtzV49QXHSzW5Fqp7JkRypLIub7Zf9xpi3eSVJmPhYL3NsrjKcGtamRo0zpuYW8tXo/CzcmUGIzcHGycE+vMP5yYyv8PLVauYi9qCzZkcqSSM2QlV/Ea8v28dnmowCE1ffkzdGdasw90gqLbXwYHc8/fjxAdn7pzW5vatuIp4dEEt7Ay+R0InWPypIdqSyJ1Cw/xaYx/atdJJ8ZZZrQO5wpA9vg4WrOJfeGYbBybyozv99H/Jn73kU29mXG0Mhau/SBiCNQWbIjlSWRmicrv4hXlu7liy3HAAhv4MXs0R3p2sy+o0x7kjJ5eelefj2UDkADbzemDGzNbV1DcNL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(ignored)\n" + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The job generate_structures_6042e2f1a150e0a8dc69909c3357e0e7 was saved and received the ID: 4\n", - "The job get_dict_773687310289049123463477639b2e74 was saved and received the ID: 5\n", - "The job calculate_qe_4d5f495146ebc0ce797711eb10c9a42c was saved and received the ID: 6\n" + "The job generate_structures_3d675d4377e56d5ea0701d9bc6ca2537 was saved and received the ID: 7\n", + "The job get_dict_7395f450ba5bbeb8b3b5f7f04cd1aec9 was saved and received the ID: 8\n", + "The job calculate_qe_9b97908b38e6b32fec79d75a790d84c8 was saved and received the ID: 9\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[thinkpad:381043] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_29ff39cf860e1a7f4c8cc7d874b08a8d was saved and received the ID: 7\n", - "The job calculate_qe_cd318257eb4a8785b66151e9a85c382c was saved and received the ID: 8\n" + "The job get_dict_7dad0bf1f915918f5b44eec63656d045 was saved and received the ID: 10\n", + "The job calculate_qe_654811b39aaefd7f278e1e4d074d8e8a was saved and received the ID: 11\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[thinkpad:381257] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_e3270a64b9a4153a91d4a851e538ad44 was saved and received the ID: 9\n", - "The job calculate_qe_b4bb4178ec80b7b49d05982d47a707f4 was saved and received the ID: 10\n" + "The job get_dict_dc6498824bbcffe1870502beda1e99ce was saved and received the ID: 12\n", + "The job calculate_qe_0bc9039e5d46cf41c8a5f8bddf5445dc was saved and received the ID: 13\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[thinkpad:381487] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_b526e8a023fce1c4a4b8588b668f16e8 was saved and received the ID: 11\n", - "The job calculate_qe_71e06766387aec54f502114cdbbb1e85 was saved and received the ID: 12\n" + "The job get_dict_c9b3348d986cce1499e3c6382b6af93e was saved and received the ID: 14\n", + "The job calculate_qe_259189d1dff5a59e2e8184e71d9597d7 was saved and received the ID: 15\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[thinkpad:381880] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_8b465dbd4ff6930762a35eac7d987f8e was saved and received the ID: 13\n", - "The job calculate_qe_716b050690854b48cae2bfb572cc5eb6 was saved and received the ID: 14\n" + "The job get_dict_3c8d628835f56af4881e577db8c861ab was saved and received the ID: 16\n", + "The job calculate_qe_f998ab33e0425ea3403460a5d2b17552 was saved and received the ID: 17\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[thinkpad:382337] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n" + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The job get_list_a28a868564f34fcd434fb31fd2b74340 was saved and received the ID: 15\n", - "The job get_list_d39a55ccd20cacf3dbeb1cfe70d08ed4 was saved and received the ID: 16\n", - "The job plot_energy_volume_curve_6f4c92d25a5f876513d595238396fafa was saved and received the ID: 17\n" + "The job get_list_2e2c8977ec516980a06d35fe27dacb7e was saved and received the ID: 18\n", + "The job get_list_a67fcac31c6773ce51be0e8903d33c68 was saved and received the ID: 19\n", + "The job plot_energy_volume_curve_c282015f1c661a67154d2e1f50e9c167 was saved and received the ID: 20\n" ] }, { "data": { - "image/png": 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", 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" ] diff --git a/jobflow_qe.ipynb b/jobflow_qe.ipynb index ddcbd5f..af7cc65 100644 --- a/jobflow_qe.ipynb +++ b/jobflow_qe.ipynb @@ -27,18 +27,7 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jan/mambaforge/lib/python3.12/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n", - " \"cipher\": algorithms.TripleDES,\n", - "/home/jan/mambaforge/lib/python3.12/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n", - " \"class\": algorithms.TripleDES,\n" - ] - } - ], + "outputs": [], "source": [ "from jobflow import job, Flow" ] @@ -200,7 +189,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"nodes\": {\"0\": \"quantum_espresso_workflow.get_bulk_structure\", \"1\": \"quantum_espresso_workflow.calculate_qe\", \"2\": \"quantum_espresso_workflow.generate_structures\", \"3\": \"quantum_espresso_workflow.calculate_qe\", \"4\": \"quantum_espresso_workflow.calculate_qe\", \"5\": \"quantum_espresso_workflow.calculate_qe\", \"6\": \"quantum_espresso_workflow.calculate_qe\", \"7\": \"quantum_espresso_workflow.calculate_qe\", \"8\": \"quantum_espresso_workflow.plot_energy_volume_curve\", \"9\": \"Al\", \"10\": 4.05, \"11\": true, \"12\": \"mini\", \"13\": \"python_workflow_definition.shared.get_dict\", \"14\": {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}, \"15\": [3, 3, 3], \"16\": \"vc-relax\", \"17\": 0.02, \"18\": [0.9, 0.9500000000000001, 1.0, 1.05, 1.1], \"19\": \"strain_0\", \"20\": \"python_workflow_definition.shared.get_dict\", \"21\": \"scf\", \"22\": \"strain_1\", \"23\": 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\"quantum_espresso_workflow.generate_structures\", \"3\": \"quantum_espresso_workflow.calculate_qe\", \"4\": \"quantum_espresso_workflow.calculate_qe\", \"5\": \"quantum_espresso_workflow.calculate_qe\", \"6\": \"quantum_espresso_workflow.calculate_qe\", \"7\": \"quantum_espresso_workflow.calculate_qe\", \"8\": \"quantum_espresso_workflow.plot_energy_volume_curve\", \"9\": \"Al\", \"10\": 4.05, \"11\": true, \"12\": \"mini\", \"13\": \"python_workflow_definition.shared.get_dict\", \"14\": {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}, \"15\": [3, 3, 3], \"16\": \"vc-relax\", \"17\": 0.02, \"18\": [0.9, 0.9500000000000001, 1.0, 1.05, 1.1], \"19\": \"strain_0\", \"20\": \"python_workflow_definition.shared.get_dict\", \"21\": \"scf\", \"22\": \"strain_1\", \"23\": \"python_workflow_definition.shared.get_dict\", \"24\": \"strain_2\", \"25\": \"python_workflow_definition.shared.get_dict\", \"26\": \"strain_3\", \"27\": \"python_workflow_definition.shared.get_dict\", \"28\": \"strain_4\", \"29\": \"python_workflow_definition.shared.get_dict\", \"30\": \"python_workflow_definition.shared.get_list\", \"31\": \"python_workflow_definition.shared.get_list\"}, \"edges\": [{\"tn\": 0, \"th\": \"element\", \"sn\": 9, \"sh\": null}, {\"tn\": 0, \"th\": \"a\", \"sn\": 10, \"sh\": null}, {\"tn\": 0, \"th\": \"cubic\", \"sn\": 11, \"sh\": null}, {\"tn\": 1, \"th\": \"working_directory\", \"sn\": 12, \"sh\": null}, {\"tn\": 13, \"th\": \"structure\", \"sn\": 0, \"sh\": null}, {\"tn\": 13, \"th\": \"pseudopotentials\", \"sn\": 14, \"sh\": null}, {\"tn\": 13, \"th\": \"kpts\", \"sn\": 15, \"sh\": null}, {\"tn\": 13, \"th\": \"calculation\", \"sn\": 16, \"sh\": null}, {\"tn\": 13, \"th\": \"smearing\", \"sn\": 17, \"sh\": null}, {\"tn\": 1, \"th\": \"input_dict\", \"sn\": 13, \"sh\": null}, {\"tn\": 2, \"th\": \"structure\", \"sn\": 1, \"sh\": \"structure\"}, {\"tn\": 2, \"th\": \"strain_lst\", \"sn\": 18, \"sh\": null}, {\"tn\": 3, \"th\": \"working_directory\", \"sn\": 19, \"sh\": null}, {\"tn\": 20, \"th\": \"structure\", \"sn\": 2, \"sh\": \"s_0\"}, {\"tn\": 20, \"th\": \"pseudopotentials\", \"sn\": 14, \"sh\": null}, {\"tn\": 20, \"th\": \"kpts\", \"sn\": 15, \"sh\": null}, {\"tn\": 20, \"th\": \"calculation\", \"sn\": 21, \"sh\": null}, {\"tn\": 20, \"th\": \"smearing\", \"sn\": 17, \"sh\": null}, {\"tn\": 3, \"th\": \"input_dict\", \"sn\": 20, \"sh\": null}, {\"tn\": 4, \"th\": \"working_directory\", \"sn\": 22, \"sh\": null}, {\"tn\": 23, \"th\": \"structure\", \"sn\": 2, \"sh\": \"s_1\"}, {\"tn\": 23, \"th\": \"pseudopotentials\", \"sn\": 14, \"sh\": null}, {\"tn\": 23, \"th\": \"kpts\", \"sn\": 15, \"sh\": null}, {\"tn\": 23, \"th\": \"calculation\", \"sn\": 21, \"sh\": null}, {\"tn\": 23, \"th\": \"smearing\", \"sn\": 17, \"sh\": null}, {\"tn\": 4, \"th\": \"input_dict\", \"sn\": 23, \"sh\": null}, {\"tn\": 5, \"th\": \"working_directory\", \"sn\": 24, \"sh\": null}, {\"tn\": 25, \"th\": \"structure\", \"sn\": 2, \"sh\": \"s_2\"}, {\"tn\": 25, \"th\": \"pseudopotentials\", \"sn\": 14, \"sh\": null}, {\"tn\": 25, \"th\": \"kpts\", \"sn\": 15, \"sh\": null}, {\"tn\": 25, \"th\": \"calculation\", \"sn\": 21, \"sh\": null}, {\"tn\": 25, \"th\": \"smearing\", \"sn\": 17, \"sh\": null}, {\"tn\": 5, \"th\": \"input_dict\", \"sn\": 25, \"sh\": null}, {\"tn\": 6, \"th\": \"working_directory\", \"sn\": 26, \"sh\": null}, {\"tn\": 27, \"th\": \"structure\", \"sn\": 2, \"sh\": \"s_3\"}, {\"tn\": 27, \"th\": \"pseudopotentials\", \"sn\": 14, \"sh\": null}, {\"tn\": 27, \"th\": \"kpts\", \"sn\": 15, \"sh\": null}, {\"tn\": 27, \"th\": \"calculation\", \"sn\": 21, \"sh\": null}, {\"tn\": 27, \"th\": \"smearing\", \"sn\": 17, \"sh\": null}, {\"tn\": 6, \"th\": \"input_dict\", \"sn\": 27, \"sh\": null}, {\"tn\": 7, \"th\": \"working_directory\", \"sn\": 28, \"sh\": null}, {\"tn\": 29, \"th\": \"structure\", \"sn\": 2, \"sh\": \"s_4\"}, {\"tn\": 29, \"th\": \"pseudopotentials\", \"sn\": 14, \"sh\": null}, {\"tn\": 29, \"th\": \"kpts\", \"sn\": 15, \"sh\": null}, {\"tn\": 29, \"th\": \"calculation\", \"sn\": 21, \"sh\": null}, {\"tn\": 29, \"th\": \"smearing\", \"sn\": 17, \"sh\": null}, {\"tn\": 7, \"th\": \"input_dict\", \"sn\": 29, \"sh\": null}, {\"tn\": 30, \"th\": \"0\", \"sn\": 3, \"sh\": \"volume\"}, {\"tn\": 30, \"th\": \"1\", \"sn\": 4, \"sh\": \"volume\"}, {\"tn\": 30, \"th\": \"2\", \"sn\": 5, \"sh\": \"volume\"}, {\"tn\": 30, \"th\": \"3\", \"sn\": 6, \"sh\": \"volume\"}, {\"tn\": 30, \"th\": \"4\", \"sn\": 7, \"sh\": \"volume\"}, {\"tn\": 8, \"th\": \"volume_lst\", \"sn\": 30, \"sh\": null}, {\"tn\": 31, \"th\": \"0\", \"sn\": 3, \"sh\": \"energy\"}, {\"tn\": 31, \"th\": \"1\", \"sn\": 4, \"sh\": \"energy\"}, {\"tn\": 31, \"th\": \"2\", \"sn\": 5, \"sh\": \"energy\"}, {\"tn\": 31, \"th\": \"3\", \"sn\": 6, \"sh\": \"energy\"}, {\"tn\": 31, \"th\": \"4\", \"sn\": 7, \"sh\": \"energy\"}, {\"tn\": 8, \"th\": \"energy_lst\", \"sn\": 31, \"sh\": null}]}" ] } ], @@ -223,7 +212,7 @@ { "data": { "text/plain": [ - "Profile" + "Profile" ] }, "execution_count": 16, @@ -263,7 +252,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ae53aad4c0e45789225a46b5b096434", + "model_id": "4b2f21f2dbc74d8fa206428847590b35", "version_major": 2, "version_minor": 1 }, @@ -289,358 +278,61 @@ "name": "stderr", "output_type": "stream", "text": [ - "03/22/2025 05:25:44 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: pickle_node10,pickle_node11,pickle_node12,pickle_node13,pickle_node15,pickle_node16,pickle_node17,pickle_node18,pickle_node19,pickle_node20,pickle_node22,pickle_node23,pickle_node25,pickle_node27,pickle_node29\n", - "03/22/2025 05:26:05 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 335, 342, 349, 356, 363, 370, 377, 384, 391, 398, 405, 412, 419, 426, 433\n", - "03/22/2025 05:27:16 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node10, type: PYTHONJOB, finished.\n", - "03/22/2025 05:27:16 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node11, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:17 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node12, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:17 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node13, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:18 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node15, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:18 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node16, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:19 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node17, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:19 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node18, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:20 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node19, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:20 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node20, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:21 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node22, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:21 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node23, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:22 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node25, type: PYTHONJOB, finished.\n" + "04/01/2025 10:52:32 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_bulk_structure1\n", + "04/01/2025 10:52:33 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: get_bulk_structure1, type: PyFunction, finished.\n", + "04/01/2025 10:52:34 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict10\n", + "04/01/2025 10:52:35 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: get_dict10, type: PyFunction, finished.\n", + "04/01/2025 10:52:36 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe2\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:52:55 AM <3361007> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['energy', 'volume'])\n", + "04/01/2025 10:52:55 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: calculate_qe2, type: PyFunction, finished.\n", + "04/01/2025 10:52:56 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: generate_structures3\n", + "04/01/2025 10:52:57 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: generate_structures3, type: PyFunction, finished.\n", + "04/01/2025 10:52:58 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict11,get_dict12,get_dict13,get_dict14,get_dict15\n", + "04/01/2025 10:52:59 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: get_dict11, type: PyFunction, finished.\n", + "04/01/2025 10:52:59 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict12,get_dict13,get_dict14,get_dict15,calculate_qe4\n", + "04/01/2025 10:53:00 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: get_dict12, type: PyFunction, finished.\n", + "04/01/2025 10:53:01 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict13,get_dict14,get_dict15,calculate_qe4,calculate_qe5\n", + "04/01/2025 10:53:02 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: get_dict13, type: PyFunction, finished.\n", + "04/01/2025 10:53:03 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict14,get_dict15,calculate_qe4,calculate_qe5,calculate_qe6\n", + "04/01/2025 10:53:04 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: get_dict14, type: PyFunction, finished.\n", + "04/01/2025 10:53:04 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict15,calculate_qe4,calculate_qe5,calculate_qe6,calculate_qe7\n", + "04/01/2025 10:53:05 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: get_dict15, type: PyFunction, finished.\n", + "04/01/2025 10:53:06 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe4,calculate_qe5,calculate_qe6,calculate_qe7,calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:53:10 AM <3361007> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:53:10 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: calculate_qe4, type: PyFunction, finished.\n", + "04/01/2025 10:53:11 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe5,calculate_qe6,calculate_qe7,calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:53:15 AM <3361007> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:53:16 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: calculate_qe5, type: PyFunction, finished.\n", + "04/01/2025 10:53:16 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe6,calculate_qe7,calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:53:21 AM <3361007> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:53:21 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: calculate_qe6, type: PyFunction, finished.\n", + "04/01/2025 10:53:22 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe7,calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:53:27 AM <3361007> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:53:28 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: calculate_qe7, type: PyFunction, finished.\n", + "04/01/2025 10:53:28 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:53:34 AM <3361007> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:53:34 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: calculate_qe8, type: PyFunction, finished.\n", + "04/01/2025 10:53:35 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list16,get_list17\n", + "04/01/2025 10:53:36 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: get_list16, type: PyFunction, finished.\n", + "04/01/2025 10:53:36 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list17\n", + "04/01/2025 10:53:37 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: get_list17, type: PyFunction, finished.\n", + "04/01/2025 10:53:38 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: plot_energy_volume_curve9\n", + "04/01/2025 10:53:39 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|update_task_state]: Task: plot_energy_volume_curve9, type: PyFunction, finished.\n", + "04/01/2025 10:53:39 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", + "04/01/2025 10:53:41 AM <3361007> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51189|WorkGraphEngine|finalize]: Finalize workgraph.\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:22 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node27, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:23 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: pickle_node29, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:27:29 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_bulk_structure1\n", - "03/22/2025 05:27:31 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 500\n", - "03/22/2025 05:27:43 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: get_bulk_structure1, type: PYTHONJOB, finished.\n", - "03/22/2025 05:27:48 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict14\n", - "03/22/2025 05:27:51 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 511\n", - "03/22/2025 05:28:02 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: get_dict14, type: PYTHONJOB, finished.\n", - "03/22/2025 05:28:07 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe2\n", - "03/22/2025 05:28:10 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 522\n", - "03/22/2025 05:28:48 PM <77053> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['energy', 'volume'])\n", - "03/22/2025 05:28:49 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: calculate_qe2, type: PYTHONJOB, finished.\n", - "03/22/2025 05:28:55 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: generate_structures3\n", - "03/22/2025 05:28:57 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 533\n", - "03/22/2025 05:29:09 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: generate_structures3, type: PYTHONJOB, finished.\n", - "03/22/2025 05:29:16 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict21,get_dict24,get_dict26,get_dict28,get_dict30\n", - "03/22/2025 05:29:24 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 548, 555, 562, 569, 576\n", - "03/22/2025 05:29:52 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: get_dict21, type: PYTHONJOB, finished.\n", - "03/22/2025 05:29:52 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: get_dict24, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:29:53 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: get_dict26, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:29:53 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: get_dict28, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:29:54 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: get_dict30, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:29:59 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe4,calculate_qe5,calculate_qe6,calculate_qe7,calculate_qe8\n", - "03/22/2025 05:30:07 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 603, 610, 617, 624, 631\n", - "03/22/2025 05:30:41 PM <77053> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:30:42 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: calculate_qe4, type: PYTHONJOB, finished.\n", - "03/22/2025 05:30:47 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "03/22/2025 05:30:50 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 610, 617, 624, 631\n", - "03/22/2025 05:30:58 PM <77053> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:30:59 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: calculate_qe5, type: PYTHONJOB, finished.\n", - "03/22/2025 05:31:06 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "03/22/2025 05:31:07 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 617, 624, 631\n", - "03/22/2025 05:31:14 PM <77053> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:31:15 PM <77053> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:31:15 PM <77053> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:31:16 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: calculate_qe6, type: PYTHONJOB, finished.\n", - "03/22/2025 05:31:16 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: calculate_qe7, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:31:17 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: calculate_qe8, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:31:22 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list31,get_list32\n", - "03/22/2025 05:31:27 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 663, 670\n", - "03/22/2025 05:31:42 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: get_list31, type: PYTHONJOB, finished.\n", - "03/22/2025 05:31:42 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: get_list32, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:31:47 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: plot_energy_volume_curve9\n", - "03/22/2025 05:31:50 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 685\n", - "03/22/2025 05:32:04 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|update_task_state]: Task: plot_energy_volume_curve9, type: PYTHONJOB, finished.\n", - "03/22/2025 05:32:09 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "03/22/2025 05:32:11 PM <77053> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [328|WorkGraphEngine|finalize]: Finalize workgraph.\n" - ] - } - ], - "source": [ - "result = wg.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ { "data": { - "image/png": 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", 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By5Yteeihh9i0aRNxcXHMmTOHVatWla2j5Ovry8MPP8zzzz/PypUriY2N5U9/+hMAo0ePNuWz2tP43mEAfLLxCAXFJeaGERERqaFMHVm6HAsXLmTSpEn069cPq9XKqFGjePvtt8ueLyoqIjY2tmxEycXFhe+//55p06YxbNgwcnJyaNmyJQsWLGDIkCFl+7355ps4Ozszbtw4Tp8+TY8ePVizZg0BAQF2/4z2Nqh9EEG+7qRk5bN0RzKjujY1O5KIiEiN4xDrLNV0jrTO0h/9a+1B3lwRS/smvnw3qS8Wi8XsSCIiInZRq9ZZkupzZ/dQ3Jyt7E7MYsuRU2bHERERqXFUluq4el6ujLiqCaBFKkVERM5HZUmY0DcMgBV7UknMOG1uGBERkRpGZUmICPKlV/P6lNgMPoo+YnYcERGRGkVlSYDSRSoBPt2UwOlCLSMgIiJylsqSANAvshEh9TzIPF3E19sSzY4jIiJSY6gsCQBOVgv39goDYP6Gw2hFCRERkVIqS1JmdLcQPF2d2J+aw4a4k2bHERERqRFUlqSMn4cLt51ZxVvLCIiIiJRSWZJy7j1zv7gfY9I4cjLX3DAiIiI1gMqSlNMi0JvrWgdiGLBgg5YREBERUVmSc5xdRmDRlqPkFBSbG0ZERMRkKktyjmtbBdI80IvsgmK+3HLU7DgiIiKmUlmSc1itFsafOXdpQfQRbDYtIyAiInWXypKc16guTfFxd+bwiVx+3n/c7DgiIiKmUVmS8/Jyc+b2biEAfKBlBEREpA5TWZILurd3GBYLrDtwgoNp2WbHERERMYXKklxQSD1P+kc2AmBeVLy5YUREREyisiQVOruMwFe/JZKZV2RuGBEREROoLEmFejWvT0SQD6eLSvh8S4LZcUREROxOZUkqZLFYykaXFmw4QnGJzdxAIiIidqayJBd1y1VNCPB0ITHjNKv3pZodR0RExK5UluSi3F2cuLN7KKATvUVEpO5RWZJLMq5XM5ysFjYeTmdPUqbZcUREROxGZUkuSWM/Dwa3DwJgvkaXRESkDlFZkkt29kTvb3ckcTKnwNwwIiIidqKyJJesS2gAHZv6UVhs49NNWkZARETqBpUluWS/X0bgo1+PUKRlBEREpA5QWZLLcnOHYAJ93EjNKuD7XclmxxEREal2KktyWVydrYztoWUERESk7lBZkss2tkczXJ2sbD+awbaEU2bHERERqVYqS3LZAn3cGNqpMQDzN8SbG0ZERKSaqSxJpUzoHQ7Asp3JpGblm5xGRESk+qgsSaV0aOpHt2YBFNsMPv71iNlxREREqo3KklTahD6lo0ufbEwgv6jE5DQiIiLVQ2VJKm1gu0YE+7lzMreQ73YkmR1HRESkWqgsSaU5O1kZ1ysMKF1GwDAMcwOJiIhUA5UluSJ3XB2Cu4uVvclZbDqcbnYcERGRKqeyJFckwMuVkZ2bAFpGQEREaieVJbli488sI7BiTwrHTuWZnEZERKRqqSzJFWsT5EOflvWxGfBRtJYREBGR2kVlSarE2dGlTzclkFdYbHIaERGRqqOyJFXixoiGhNbzJCu/mK+3JZodR0REpMqoLEmVcLJauLd3GADztYyAiIhUkd8STvHNtkRT/66oLEmVGd2tKV6uThxIy2H9wRNmxxEREQeXX1TCk1/s4LHPt5t6xbXKklQZX3cXbuvaFChdpFJERORKzFkZy6ETuTTydePWzk1Ny6GyJFXq7FTcmpg0Dp/INTeMiIg4rC3x6fxv/WEAZt7aAT9PF9OyqCxJlWoe6M0NbQIBWKBFKkVEpBJOF5Yw5cudGAbc1rUpN0Y0MjWPypJUuQl9SpcR+HLrMbLzi0xOIyIijubNFbEcPpFLkK87M4a2NTuOypJUvWtaNaBFoBc5BcUs2nLM7DgiIuJANh1OZ96GM9Nvozrg52He9NtZKktS5SwWC+PPjC4tiI7HZtMyAiIicnF5hcU89eUODAPGdGvKDW0amh0JUFmSajKqSxN83Z05cjKPtbFpZscREREH8MbyWOJP5tHYz51na8D021kqS1ItPF2duaN7KKBlBERE5OJ+PXSybC2lWaM64utu/vTbWSpLUm3G9WyG1QLrD55gf2q22XFERKSGKp1+2wnAHVeHcF3rQJMTlaeyJNUmpJ4nN7UtvdzTzJVXRUSkZnv9hxgS0vMI9nPnmZsjzY5zDpUlqVZnlxH46rdjZOQVmpxGRERqmui4kyyIPgLA67d1xKcGTb+dpbIk1apHeD0iG/uSX2Tjs81HzY4jIiI1SG5BMVO+3AHAnd1DuaZVzZp+O0tlSaqVxWJhwplboHwUfYTiEpu5gUREpMaY9UMMx06dpom/R42cfjtLZUmq3fCrgqnn5UpixmlW7U01O46IiNQAGw6e4KNfS6ff3ritI95uziYnujCVJal27i5O3KVlBERE5IycgmKmnLn6bWyPUPq0bGByooqpLIld3N2zGc5WC5vi09mdmGl2HBERMdHM7/eRmHGapgEeTB9Sc6ffzlJZErsI8nNncIfGgJYREBGpy9YfOMHCjQlAzZ9+O0tlSexmQp8wAJZsT+JEToG5YURExO6y84uYurh0+u2eXs3o3aJmT7+d5TBlKT09nbFjx+Lr64u/vz8TJ04kJyenwn1SUlIYN24cQUFBeHl50aVLFxYvXlzuNfv37+eWW26hQYMG+Pr60rdvX9auXVudH6XO6hIaQKcQfwpLbHxy5l8VIiJSd7z2fQyJGacJqefB1EERZse5ZA5TlsaOHcuePXtYtWoVS5cu5ZdffuHBBx+scJ977rmH2NhYlixZwq5du7j11lsZM2YM27ZtK3vN0KFDKS4uZs2aNWzdupVOnToxdOhQUlJSqvsj1Un3nRld+ujXIxQWaxkBEZG64pf9x/l005npt1Gd8HKA6bezHKIs7du3j+XLl/O///2PHj160LdvX9555x0+++wzkpKSLrjfhg0bePTRR+nevTvNmzfn2Wefxd/fn61btwJw4sQJDhw4wLRp0+jYsSOtWrVi1qxZ5OXlsXv3bnt9vDplcPvGNPRx43h2AT/sTjY7joiI2EFWfhHTzky/je8dRq8W9U1OdHkcoixFR0fj7+9Pt27dyrb1798fq9XKxo0bL7hf7969+fzzz0lPT8dms/HZZ5+Rn5/P9ddfD0D9+vVp06YNH374Ibm5uRQXF/Puu+/SsGFDunbtWt0fq05ydbZyd89mAHygZQREROqEV5fuIykzn2b1PXlqUBuz41w2hxgDS0lJoWHDhuW2OTs7U69evQqny7744gtuv/126tevj7OzM56ennz99de0bNkSKF1devXq1YwYMQIfHx+sVisNGzZk+fLlBAQEXPB9CwoKKCj4/xOUs7KyrvAT1i139Qjln2sOsuNoBr8lnKJL6IWPtYiIOLafYtP4fEvp7a7eGNURT1eHqB7lmDqyNG3aNCwWS4WPmJiYSr//jBkzyMjIYPXq1WzZsoUnnniCMWPGsGvXLgAMw+DPf/4zDRs2ZN26dWzatIkRI0YwbNgwkpMvPEU0c+ZM/Pz8yh4hISGVzlgXNfB2Y/hVwYAWqRQRqc0yTxcxbXHp39wJfcLo0dyxpt/OshiGYZj1zY8fP87JkycrfE3z5s35+OOPmTx5MqdOnSrbXlxcjLu7O4sWLWLkyJHn7BcXF0fLli3ZvXs37dq1K9vev39/WrZsydy5c/nxxx8ZMGAAp06dwtfXt+w1rVq1YuLEiUybNu28mc43shQSEkJmZma595EL252YydB31uNstbB+6o0E+bmbHUlERKrYlEU7WLT1GGH1Pfnhr9fi4epkdqRysrKy8PPzu+jfb1PHwgIDAwkMvPgdhnv16kVGRgZbt24tO5dozZo12Gw2evTocd598vLyALBayw+eOTk5YbPZKnyN1Wote835uLm54ebmdtHccmHtm/jRPawem+LT+fjXIzw50PHmsEVE5MLWxqSxaOsxLBZ4c3SnGleULodDnOAdGRnJoEGDeOCBB9i0aRNRUVFMmjSJO+64g+Dg0umcxMREIiIi2LRpEwARERG0bNmShx56iE2bNhEXF8ecOXNYtWoVI0aMAEpLWEBAAPfeey87duxg//79TJkyhcOHD3PzzTeb9XHrjLOLVH6yKYH8ohJzw4iISJXJzCti2lelV7/d1yecq8PqmZzoyjhEWQJYuHAhERER9OvXjyFDhtC3b1/ee++9sueLioqIjY0tGy1ycXHh+++/JzAwkGHDhtGxY0c+/PBDFixYwJAhQwBo0KABy5cvJycnhxtvvJFu3bqxfv16vv32Wzp16mTK56xLbmrbiCb+HqTnFrJk+4WXgBAREcfy0tK9pGYVEN7AiycHOP7MgannLNUWlzrnKeea+3Mcs36IIbKxL9//pS8Wi8XsSCIicgV+3JfKxAVbsFjgy4d70bVZzR1VutS/3w4zsiS10x1Xh+DuYmVfchYbD6ebHUdERK5AZl4R078qvfrt/r7hNbooXQ6VJTGVv6crt3ZpCsC8qMMmpxERkSvx4nd7SMsuoHmgF5NrwfTbWSpLYroJvcMAWLU3laPpeeaGERGRSlm1N5WvtiVitcDs0Z1wd3Hcq9/+SGVJTNeqkQ99WzbAZpTeYFdERBxLRl4hT39dOv32wDXNa92dGVSWpEY4u4zAZ5sSyCssNjeMiIhclheW7OF4dgEtAr14/KbWZsepcipLUiPc0KYhYfU9ycovZvFviWbHERGRS7RiTwrfbE/CaoE5Y66qVdNvZ6ksSY1gtVq498y5S/OjDmOzaUULEZGa7lRuIc98vRuAh65rwVUh/uYGqiYqS1Jj3Na1Kd5uzsQdz2X9wRNmxxERkYt4fskeTuQU0KqhN4/1b2V2nGqjsiQ1ho+7C7d11TICIiKOYPnuZJbsSMLJamH26E64Ode+6bezVJakRhnfOwyLBdbGHufQ8Ryz44iIyHmk5xby7Del028PX9ecTrV0+u0slSWpUcIaeHFjm4YALNgQb24YERE5r+e+3c2JnELaNPLhL/1q7/TbWSpLUuOMP7OMwJdbj5GVX2RuGBERKef7Xcks3ZlcJ6bfzlJZkhqnb8sGtGroTW5hCYu2HDM7joiInHEip6Bs+u2R61vQoamfyYnso1JlKTc3t6pziJSxWCxlo0sLNsRTomUERERqhOe+3U16biERQT48emPtn347q1JlqVGjRtx3332sX7++qvOIADCycxP8PFxISM9jTUya2XFEROq8pTuT+H5XCs5npt9cnevO5FSlPunHH39Meno6N954I61bt2bWrFkkJSVVdTapwzxdnbnj6hAA5m/QMgIiImY6nl3AjLPTbze0pH2TujH9dlalytKIESP45ptvSExM5OGHH+aTTz6hWbNmDB06lK+++oriYt3bS67cuF7NsFog6uBJYlOyzY4jIlInGYbBjG92cyqviMjGvky6oaXZkezuisbQAgMDeeKJJ9i5cyd/+9vfWL16NbfddhvBwcE899xz5OXlVVVOqYOaBngysF0QoNElERGzfLczmeV7zk6/daxT029nXdEnTk1N5Y033qBt27ZMmzaN2267jR9//JE5c+bw1VdfMWLEiCqKKXXVhD7hAHz1WyKncgtNTiMiUrekZefz3Lel02+TbmxJu+C6Nf12lnNldvrqq6+YN28eK1asoG3btjzyyCPcfffd+Pv7l72md+/eREZGVlVOqaOuDgugbWNf9iZn8dnmo/zp+hZmRxIRqRMMw+DZr3eTkVdE28a+/LkOTr+dVamRpQkTJhAcHExUVBTbt29n0qRJ5YoSQHBwMM8880xVZJQ6zGKxMOHMMgIfRcdTXGIzN5CISB2xZEcSK/em4uJkYc6YTrg41b3pt7MqNbKUnJyMp6dnha/x8PDg+eefr1Qokd8b1imYWT/EkJSZz4o9qdzcsbHZkUREarW0rHye+3YPAI/e2IrIxr4mJzJXpWpicXExWVlZ5zyys7MpLNR5JVK13F2cGNsjFIB5UTrRW0SkOhmGwdNf7yLzdBHtm/jq9AcqWZb8/f0JCAg45+Hv74+HhwfNmjXj+eefx2bTlIlUjbE9m+FstbDlyCl2Hcs0O46ISK319bZEVu9Lw8WpdPHJujz9dlaljsD8+fMJDg7m6aef5ptvvuGbb77h6aefpkmTJvznP//hwQcf5O2332bWrFlVnVfqqEa+7mXTb/O0jICISLVIzcrnhSWl02+P9W9NRFDdnn47q1LnLC1YsIA5c+YwZsyYsm3Dhg2jQ4cOvPvuu/z444+Ehoby6quv8vTTT1dZWKnbJvQJ59vtSSzdkcz0wZEE+riZHUlEpNYwDIOnv9pFVn4xHZv68dC1zc2OVGNUamRpw4YNdO7c+ZztnTt3Jjo6GoC+ffuSkJBwZelEfueqEH86h/pTWGJj4cYjZscREalVFv+WyI8xabg6WZk9uhPOmn4rU6kjERISwvvvv3/O9vfff5+QkNL7eZ08eZKAgIArSyfyB+N7hwHw8a8JFBbrnDgRkaqQkpnPi9+dmX67qRWtG/mYnKhmqdQ03OzZsxk9ejQ//PADV199NQBbtmwhJiaGL7/8EoDNmzdz++23V11SEWBIh8a89v0+UrMKWLYriZGdm5odSUTEoRmGwfSvdpKdX0ynEH8evEbTb39UqZGl4cOHExsby5AhQ0hPTyc9PZ3BgwcTExPD0KFDAfjTn/7E3/72tyoNK+LiZGVcz2YAzIuKxzAMkxOJiDi2RVuPsTb2eOn0220dNf12Hpc9slRUVMSgQYOYO3cuM2fOrI5MIhW6s3sob685yM5jmfyWcIquzeqZHUlExCElZ57m5e/2AvDEgNa00vTbeV12fXRxcWHnzp3VkUXkktT3duOWTsFA6eiSiIhcPsMwmLp4F9kFxVwV4s8Dmn67oEqNtd19993nPcFbxF4m9AkH4IfdKSRnnjY5jYiI4/liy1F+2X8cV+fSq9+crBazI9VYlTrBu7i4mA8++IDVq1fTtWtXvLy8yj2vc5WkurUN9qVHeD02Hk7no+gjPDUowuxIIiIOIzHjNK8s3QfAkwNa07Kht8mJarZKlaXdu3fTpUsXAPbv31/uOYtFzVTsY0KfcDYeTufTTQn8pV8r3F2czI4kIlLjGYbBtMU7yS4opkuoPxP7avrtYipVltauXVvVOUQu201tG9HE34PEjNN8uz2R268ONTuSiEiN99nmo6w7cAI3ZytvavrtklzR9YEHDx5kxYoVnD5des6ILuMWe3KyWri3t5YREBG5VMdO5fHK0tKr36YMbEOLQE2/XYpKlaWTJ0/Sr18/WrduzZAhQ0hOTgZg4sSJTJ48uUoDilTk9m6heLg4EZOSTfShk2bHERGpsUqn33aRW1hCt2YBZRfKyMVVqiw9/vjjuLi4kJCQgKenZ9n222+/neXLl1dZOJGL8fN0YVTXJoCWERARqcgnmxJYf/AE7i6afrtclSpLK1eu5PXXX6dp0/K3mmjVqhVHjugGp2JfZ+8Xt3pfKkfT88wNIyJSAx1Nz+O1ZaVXv00ZGEF4A6+L7CG/V6mylJubW25E6az09HTc3NyuOJTI5WjZ0IdrWjXAMGDBhniz44iI1Cg2m8HUxTvJLSyhe1g9Jpz5B6ZcukqVpWuuuYYPP/yw7GuLxYLNZuONN97ghhtuqLJwIpfqvjNz759vOUpuQbHJaUREao6FmxLYEHcSdxcrb9zWEaum3y5bpZYOeOONN+jXrx9btmyhsLCQp556ij179pCenk5UVFRVZxS5qOtaBxLewIvDJ3JZ/Nsx7ukVZnYkERHTHU3PY+b3pdNvUwdFEKbpt0qp1MhS+/bt2b9/P3379uWWW24hNzeXW2+9lW3bttGiRYuqzihyUVarhXt7lS4jMH9DPDablhEQkbrNZjOY8uUO8gpL6B5ej3v1j8hKq9TIEoCfnx/PPPNMVWYRuSK3dQthzsr9HDqeyy8HjnN9m4ZmRxIRMc1Hvx7h10PpeLg4Mfu2Tpp+uwKVLksZGRls2rSJtLQ0bDZbuefuueeeKw4mcrm83ZwZ3S2ED6IOMy8qXmVJROqsIydzmfVDDADTh0QQWv/ci7Lk0lWqLH333XeMHTuWnJwcfH19y90PzmKxqCyJae7t3Yx5Gw7z8/7jHEzL0c0hRaTOKZ1+28npohJ6Nq/H3T2amR3J4VXqnKXJkydz3333kZOTQ0ZGBqdOnSp7pKenV3VGkUvWrL4X/SJKR5Q+jI43N4yIiAkWRMez6XA6nq5OvKnptypRqbKUmJjIX/7yl/OutSRitrNL+H+59RiZp4tMTiMiYj/xJ3J5ffnZ6bdIQurp73RVqFRZGjhwIFu2bKnqLCJVoneL+rRp5ENeYQmLthw1O46IiF2cvfotv8hG7xb1Gds91OxItUalzlm6+eabmTJlCnv37qVDhw64uLiUe3748OFVEk6kMiwWC+P7hDH9q13M3xDPhD7hugeSiNR68zbEszn+FF6uTrw+SotPViWLYRiXvSCN1XrhASmLxUJJSckVhXI0WVlZ+Pn5kZmZia+vr9lxBDhdWEKvWT+SkVfEe+O6MqBdkNmRRESqzaHjOQx5ex35RTZeHdmesTqp+5Jc6t/vSk3D2Wy2Cz7qWlGSmsnD1Yk7ri4dgp4XFW9uGBGRalRy5uq3/CIbfVs24C5Nv1W5yypLQ4YMITMzs+zrWbNmkZGRUfb1yZMnadu2bZWFE7kS9/RqhpPVQvShk+xLzjI7johItZgXdZitR07h7ebMrFEdyi3nI1XjssrSihUrKCgoKPv6tddeK7dUQHFxMbGxsVWXTuQKBPt7MOjM9Nt8jS6JSC0UdzyHN1eU/t195uZImgbo6rfqcFll6Y+nN1XidCcRuxrfJwyAb7Ynkp5baG4YEZEqVGIzmLJoBwXFNq5p1YA7rg4xO1KtValzlkQcRbdmAbRv4ktBsY1PNyWYHUdEpMq8v/4QvyVk4OPmzOujOmr6rRpdVlmyWCzn/J+h/3OkJrNYLEzoXbpI5UfRRygqsV1kDxGRmu9gWjazV+4H4NmhkQT7e5icqHa7rHWWDMNg/PjxuLm5AZCfn8/DDz+Ml5cXQLnzmURqiqGdGjPzh32kZOWzfHcKwzoFmx1JRKTSSmwGTy7aSWGxjetaBzKmm6bfqttllaV777233Nd33333Oa/RTXSlpnFzduKuHs14+8cDzN8Qr7IkIg7tv+sOsf1oBj7uuvrNXi6rLM2bN6+6cohUq7t7hvKfnw6y9cgpdh7LoGNTf7MjiYhctgOp2fztzPTbjKFtaeyn6Td70AneUic09HFnaMfSESUtUikijqi4xMaTi3ZQWGLjhjaBjO7a1OxIdYbKktQZ43uHAbB0ZxJpWfnmhhERuUzv/nKIHccy8XF3ZuatuvrNnhymLKWnpzN27Fh8fX3x9/dn4sSJ5OTkVLhPXFwcI0eOJDAwEF9fX8aMGUNqauoVv684pk4h/nQJ9aeoxGDhRi0jICKOIzYlm3+sPgDAC8PaEeTnbnKiusVhytLYsWPZs2cPq1atYunSpfzyyy88+OCDF3x9bm4uAwYMwGKxsGbNGqKioigsLGTYsGHYbLZKv684tgl9SpcRWLjxCAXFuo+hiNR8Rb+bfusX0ZBbuzQxO1KdYzEcYBnuffv20bZtWzZv3ky3bt0AWL58OUOGDOHYsWMEB597ddPKlSsZPHgwp06dKruTcGZmJgEBAaxcuZL+/ftX6n3P51LvWizmKyqxcc3ra0nJymfO6E6M0py/iNRw/1xzgNkr9+Pr7syqJ66jka9GlarKpf79doiRpejoaPz9/csKDUD//v2xWq1s3LjxvPsUFBRgsVjK1oQCcHd3x2q1sn79+kq/rzg2Fycr43o1A2DehsO6ZY+I1GgxKVn848fS6bcXb2mnomQShyhLKSkpNGzYsNw2Z2dn6tWrR0pKynn36dmzJ15eXkydOpW8vDxyc3N58sknKSkpITk5udLvC6VFLCsrq9xDHMed3UNxc7ayOzGLrUdOmR1HROS8ikpsTP5iB0UlBv0jGzHiKk2/mcXUsjRt2rSyW6hc6BETE1Op9w4MDGTRokV89913eHt74+fnR0ZGBl26dMFqvbKPPXPmTPz8/MoeISFaPdWR1PNyLfulo2UERKSm+s9PcexJysLf04XXbm2vq99MdFmLUla1yZMnM378+Apf07x5c4KCgkhLSyu3vbi4mPT0dIKCgi6474ABA4iLi+PEiRM4Ozvj7+9PUFAQzZs3B6j0+06fPp0nnnii7OusrCwVJgczoW8Yn285yvI9KSRlnNZ9lUSkRtmblMU7a85Mvw1vR0MfTb+ZydSyFBgYSGBg4EVf16tXLzIyMti6dStdu3YFYM2aNdhsNnr06HHR/Rs0aFC2T1paGsOHD7+i93Vzcyt3LpQ4noggX3o1r0/0oZN8GH2EaYMjzI4kIgL8/9VvRSUGA9o2Yrhu0WQ6hzhnKTIykkGDBvHAAw+wadMmoqKimDRpEnfccUfZFWuJiYlERESwadOmsv3mzZvHr7/+SlxcHB9//DGjR4/m8ccfp02bNpf8vlJ7TegTBsBnmxM4XahlBESkZvjX2oPsTc4iwNOFV0fq3m81gUOUJYCFCxcSERFBv379GDJkCH379uW9994re76oqIjY2Fjy8vLKtsXGxjJixAgiIyN56aWXeOaZZ5g9e/Zlva/UXv0iGxFSz4OMvCK+2Z5odhwREfYkZfLPNQcBePGW9gT6aBajJnCIdZZqOq2z5Lj+t+4QryzbR+tG3qx47Fr9C05ETFNYbGP4P9cTk5LNoHZB/OfuLvqdVM1q1TpLItVldLcQPF2d2J+aw4a4k2bHEZE67J9rDxKTkk09L1deGamr32oSlSWp0/w8XLjtzCreWkZARMyyOzGTf60tnX576ZZ2NPDW9FtNorIkdd69vcMA+DEmlSMnc80NIyJ1TmFx6dVvJTaDmzs0ZmhHXWBU06gsSZ3XItCb61oHYhiwYMMRs+OISB3zzpoDxKRkU9/LlZduaWd2HDkPlSUR/n8ZgUVbjpJTUGxuGBGpM3Yey+DfP8UB8PKI9tTX9FuNpLIkAlzbKpDmgV5kFxSzeOsxs+OISB1QUFxSNv02tGNjhnRobHYkuQCVJRHAarUw/sy5S/M3xGOzaUUNEale/1h9gP2pOTTwduWlW9qbHUcqoLIkcsaoLk3xcXfm8Ilcft5/3Ow4IlKL7TiawdyfS6ffXhnRgXperiYnkoqoLImc4eXmzO3dSm+I/EHUYZPTiEhtlV9UOv1mM2B4p2AGtb/wjdulZlBZEvmde3uHYbHAugMnOJiWbXYcEamF3lp9gANpOTTwduPF4br6zRGoLIn8Tkg9T/pHNgJKz10SEalK2xJO8d4vpdNvr41sT4Cm3xyCypLIH5xdRmDx1kQy84rMDSMitcbvp99GXBXMgHaafnMUKksif9CreX0ignw4XVTC51sSzI4jIrXE31ftJ+54LoE+bryg6TeHorIk8gcWi6VsdGnBhiOUaBkBEblCW4+c4r/rDgEwc2QH/D01/eZIVJZEzuOWq5oQ4OlCYsZpVu1NNTuOiDiw/KISppyZfru1cxP6t21kdiS5TCpLIufh7uLEnd1DAZinZQRE5ArMWRnLoRO5NPRx4/lhmn5zRCpLIhcwrlcznKwWNh5OZ09SptlxRMQBbT2Szv/Wl/6Da9aoDvh5upicSCpDZUnkAhr7eTD4zGJxC7SMgIhcptOFJTy5aCeGUXqHgBsjNP3mqFSWRCpw9kTvb7YncTKnwNwwIuJQZq+M5fCJXBr5uvHcsLZmx5EroLIkUoEuoQF0bOpHYbGNTzdpGQERuTSbDqeX3TZp1qiO+Hlo+s2RqSyJVOD3ywh89OsRikps5gYSkRrvdGEJT325A8OAMd2ackObhmZHkiuksiRyETd3CCbQx43UrAJ+2J1idhwRqeHeWBFD/Mk8Gvu58+xQTb/VBipLIhfh6mxlbA8tIyAiF7fx0EnmRcUDpdNvvu6afqsNVJZELsHYHs1wdbKyLSGD7UczzI4jIjVQXmExU77cCcAdV4dwXetAkxNJVVFZErkEgT5uDO3UGID5Gl0SkfN4Y3ksCel5BPu588zNkWbHkSqksiRyiSb0Dgdg2a5k0rLyTU4jIjVJdNxJ5p9Zj+312zrio+m3WkVlSeQSdWjqR7dmARSVGHz86xGz44hIDZFbUMxTi3cAcGf3UK5ppem32kZlSeQyTOhTOrq0cGMC+UUlJqcRkZpg1g8xHE0/TRN/D54eEmF2HKkGKksil2Fgu0YE+7lzMreQpTuTzY4jIibbcPAEH50ZaX59lKbfaiuVJZHL4OxkZVyvMKB0GQHDMMwNJCKmySko5qnFpVe/je0RSt9WDUxOJNVFZUnkMt1xdQjuLlb2JGWxOf6U2XFExCQzv9/HsVOnaRrgwfQhuvqtNlNZErlMAV6ujOzcBNAilSJ11foDJ1i4sfR+kW/c1hFvN2eTE0l1UlkSqYTxZ5YRWLEnhcSM0yanERF7ys4vYuqZ6bdxPZvRu4Wm32o7lSWRSmgT5EOflvWxGfBhdLzZcUTEjl77PobEjNOE1PNg2mBd/VYXqCyJVNLZ0aXPNh0lr7DY5DQiYg+/7D/Op5vOTL+N6oSXpt/qBJUlkUq6MaIhofU8yTxdxNfbEs2OIyLVLCu/iGlnpt/u7dWMXi3qm5xI7EVlSaSSnKwW7u0dBsD8qHgtIyBSy722bB9JmfmE1vNkqqbf6hSVJZErMLpbU7xcnTiQlkPUwZNmxxGRavJTbBqfbT4KwJu3dcTTVdNvdYnKksgV8HV34bauTQEtIyBSW2XlFzH9q10ATOgTRo/mmn6ra1SWRK7Q2am4NbFpxJ/INTeMiFS5V5buJTkzn7D6njw1UNNvdZHKksgVah7ozQ1tAjEMWKBlBERqlbUxaXyx5RgWC7w5uhMerk5mRxITqCyJVIEJfUqXEVi05RjZ+UUmpxGRqpB5uohpX5Ve/XZfn3CuDqtnciIxi8qSSBW4plUDWgR6kVNQzJdbj5kdR0SqwMtL95KaVUB4Ay+eHNDG7DhiIpUlkSpgsVgYf2Z0acGGeGw2LSMg4sjWxKTy5dbS6bfZoztq+q2OU1kSqSKjujTB192Z+JN5/LQ/zew4IlJJmXlFTFtcevXb/X3D6dpM0291ncqSSBXxdHXmju6hAMyLijc3jIhU2ovf7SEtu4DmgV5M1vSboLIkUqXG9WyG1QLrDpzgQGq22XFE5DKt3pvKV9sSsVpg9uhOuLto+k1UlkSqVEg9T25q2wiAeRvizQ0jIpclI6+Q6V+XTr89cE1zuoQGmJxIagqVJZEqdnYZga9+O0ZmnpYREHEULyzZw/HsAloEevH4Ta3NjiM1iMqSSBXrEV6PyMa+5BfZ+GxzgtlxROQSrNiTwjfbkzT9JuelsiRSxSwWCxPO3ALlw+gjFJfYzA0kIhU6lVvIM1/vBuDBa1vQWdNv8gcqSyLVYPhVwdTzciUx4zSr9qaaHUdEKvD8kj2cyCmgVUNvHuvfyuw4UgOpLIlUA3cXJ+46s4zA/9Yf1uiSSA21fHcyS3Yk4WS1aPpNLkhlSaSajOvVDGerha1HTjFqbjQH07SUgEhNkp5byLPflE6/PXRtczqF+JsbSGoslSWRatLI151/3NEZH3dndhzNYMjb63n35zhKdCsUkRrhuW93cyKnkNaNvPmrpt+kAipLItXo5o6NWfX4ddzQJpDCYhszf4jhtrkbiDueY3Y0kTrt+13JLN2ZjJPVwpzRV+HmrOk3uTCVJZFqFuTnzgfjr+aN2zri4+bMtoQMhvxjHf/95ZBGmURMcDKngBlnpt8eub4FHZr6mZxIajqVJRE7sFgsjOkWwsonruW61oEUFNt49ft9jHk3mkMaZRKxq+e+3cPJ3EIignx49EZNv8nFqSyJ2FFjPw/mT7ia10d1wMfNma1HTjH4H+v43zqNMonYw9KdSSzblVx29Zurs/4MysXpp0TEziwWC7dfHcqKx6/lmlYNKCi28cqyfdz+bjSHT+SaHU+k1jqRU8Bz3+4B4M83tKR9E02/yaVRWRIxSbC/Bx/e152Zt3bA282ZLUdOMfgfv/DB+sPYNMokUqUMw2DGN7tJzy0ksrEvk25oaXYkcSAqSyImslgs3Nm9dJSpb8sG5BfZeGnpXu5471fiNcokUmW+25nMD7tTcLZamD26o6bf5LLop0WkBmji78FHE7vz6sj2eLk6sSk+nUH/+IX5URplErlSadn5PPdt6dVvk25sSbtgTb/J5XGYspSens7YsWPx9fXF39+fiRMnkpNT8VVEcXFxjBw5ksDAQHx9fRkzZgypqf9/n674+HgmTpxIeHg4Hh4etGjRgueff57CwsLq/jgi57BYLIzt0Yzlj11L7xb1yS+y8cJ3e7nzv7+ScDLP7HgiDskwDJ79ejcZeUW0bezLnzX9JpXgMGVp7Nix7Nmzh1WrVrF06VJ++eUXHnzwwQu+Pjc3lwEDBmCxWFizZg1RUVEUFhYybNgwbLbS+3TFxMRgs9l499132bNnD3//+9+ZO3cuTz/9tL0+lsg5Qup58vHEHrw8oj2erk5sPJzOwLd+YcGGeI0yiVyG7PwiXlm2j5V7U3FxKr36zcXJYf7sSQ1iMQyjxv/23bdvH23btmXz5s1069YNgOXLlzNkyBCOHTtGcHDwOfusXLmSwYMHc+rUKXx9fQHIzMwkICCAlStX0r9///N+rzfffJP//Oc/HDp06JLzZWVl4efnR2ZmZtn3EqkKR9PzeOrLnUQfOglAz+b1ePO2ToTU8zQ5mUjNVVxi4/MtR/nbyv2czC2dKXhqUBseuV6jSlLepf79doiKHR0djb+/f1lRAujfvz9Wq5WNGzeed5+CggIsFgtubm5l29zd3bFaraxfv/6C3yszM5N69epVXXiRKxBSz5OF9/fg5Vva4eHixK+HSkeZPorWKJPI+aw7cJyb317PM1/v5mRuIc0DvXj/3m786boWZkcTB+YQZSklJYWGDRuW2+bs7Ey9evVISUk57z49e/bEy8uLqVOnkpeXR25uLk8++SQlJSUkJyefd5+DBw/yzjvv8NBDD1WYp6CggKysrHIPkepitVoY1yuMFY9dS4/weuQVljDj2z3c/f5GjqbrXCYRgINpOUyYt4lx728iNjUbPw8Xnh/WlhWPXUu/yEZYLBazI4oDM7UsTZs2DYvFUuEjJiamUu8dGBjIokWL+O677/D29sbPz4+MjAy6dOmC1Xrux05MTGTQoEGMHj2aBx54oML3njlzJn5+fmWPkJCQSmUUuRyh9T359IGevDi8dJRpQ9xJBr31Cx//egQHmE0XqRancgt5/tvdDHzrF9bGHsfZamFCnzB+nnI9E/qE6xwlqRKmnrN0/PhxTp48WeFrmjdvzscff8zkyZM5depU2fbi4mLc3d1ZtGgRI0eOrPA9Tpw4gbOzM/7+/gQFBTF58mSmTJlS9nxSUhLXX389PXv2ZP78+ectU79XUFBAQUFB2ddZWVmEhITonCWxmyMnc5myaCeb4tMB6NuyAbNGdaBpgM5lkrqhsNjGh9HxvP3jAbLyiwHoH9mQ6UMiaRHobXI6cRSXes6SQ53gvWXLFrp27QqUnsA9aNCgC57gfT5r1qyhf//+7Nu3jzZt2gClI0o33HADXbt25eOPP8bJyemy8+kEbzGDzWawIDqe15fHkF9kw9vNmaeHRHJn9xBNOUitZRgGq/amMvOHmLLbA0UE+TBjaFv6tGxgcjpxNLWqLAEMHjyY1NRU5s6dS1FRERMmTKBbt2588sknQGnp6devHx9++CHdu3cHYN68eURGRhIYGEh0dDR//etfGT9+PHPmzCnb5/rrr6dZs2YsWLCgXFEKCgq65GwqS2KmwydyeerLHWyOLx15vaZVA2aN6kgTfw+Tk4lUrb1JWbyybC8b4kpnJBp4u/HkgNaM7haCk1X/QJDLd6l/v53tmOmKLFy4kEmTJtGvXz+sViujRo3i7bffLnu+qKiI2NhY8vL+/4TX2NhYpk+fTnp6OmFhYTzzzDM8/vjjZc+vWrWKgwcPcvDgQZo2bVru+zlIhxQhvIEXnz3Yi/kb4nljeQzrDpxg4N9/4dmbI7n9ao0yieNLy85nzor9fLH1KIYBrs5W7u8bziM3tMTbzWH+jIkDc5iRpZpMI0tSUxw6nsOUL3ey9UjpKNO1rQOZdWsHgjXKJA4ov6iE99cf5t9rD5JbWALA0I6NmTooQmuNSZWoddNwNZnKktQkJTaDeVGHeXNFLAXFNnzcnJkxtC2juzXVKJM4BMMw+G5nMq//EENixmkAOoX489zQSLo20zp4UnVUluxIZUlqorjjOTy5aAfbEjIAuL5NIDNv7UBjP40ySc21LeEULy/dy29nfm4b+7kzdVAEwzsFY9V5SVLFVJbsSGVJaqoSm8H76w8xe+V+Cott+Lg789zQttzWVaNMUrMkZpzmjeUxfLs9CQAPFyf+dH0LHrimOR6ul3+VssilUFmyI5UlqekOpmXz5KKdbD+aAcCNEQ15bWQHgvzczQ0mdV5uQTFzf47jvV8OUVBsw2KBUV2aMmVgGxr56udTqpfKkh2pLIkjKC6x8b/1h/nbqtJRJl93Z54b1o5RXZpolEnszmYz+PK3Y8xeEUtadukiv93D6/Hc0La0b+JncjqpK1SW7EhlSRzJgdRsnly0gx3HMgHoF9GQ127toH/Fi91Ex53klWV72ZNUel/N0HqePD0kgoHtglTcxa5UluxIZUkcTXGJjffWHeKtVQcoLCkdZXpheDtGdtYok1Sf+BO5vPb9PlbuTQXAx82ZR/u15N7eYbg567wksT+VJTtSWRJHtT81m8lf7GBXYukoU//IRrx2a3sa+miUSapO5uki3vnxAAui4ykqMbBa4K4eoTzevzX1vd3Mjid1mMqSHaksiSMrLrHx7i+HeGv1fopKDPw8XHhxeDtuuSpYo0xyRYpLbHyyKYG/r9rPqbwiAK5rHcgzN0fSupGPyelEVJbsSmVJaoOYlCyeXLSD3Yml55EMaNuIV0d2INBH//KXy7c2No1Xl+3jYFoOAK0aevPMzZFc36ahyclE/p/Kkh2pLEltUVRiY+5Pcby95gBFJQb+nqWjTMM7aZRJLs3+1GxeWbaPX/YfByDA04UnbmrNnd1DcXaympxOpDyVJTtSWZLaZl9yFpO/2MHe5NJRpkHtgnh5RHuNMskFncwp4O+r9/PppqOU2AxcnCyM7x3GpBtb4efhYnY8kfNSWbIjlSWpjYpKbPx7bRzvrDlAsc0gwNOFl0e0Z2jHYLOjSQ1SUFzCgg3xvLPmINn5xQAMbNeI6YMjCWvgZXI6kYqpLNmRypLUZnuSMnly0U72nRllGtIhiJduaU8DXcVUpxmGwfLdKcz8IYaE9DwA2gX78uzNbenVor7J6UQujcqSHaksSW1XWGzjX2sP8q+1Bym2GdTzcuXlW9pzc8fGZkcTE+w6lsnLy/ay6XA6AA193JgysA23dmmKk252Kw5EZcmOVJakrtidmMmTi3YQk5INwM0dG/PS8HZaK6eOSM3K543lsXy17RiGAW7OVh66tjkPXdcCLzdns+OJXDaVJTtSWZK6pLDYxj/XHOBfP8VRYjOo7+XKKyPaM7iDRplqq9OFJbz3yyHm/hzH6aISAEZcFcxTgyII9vcwOZ1I5aks2ZHKktRFu46VjjLFppaOMg3rFMyLw9tRz8vV5GRSVWw2g293JPLG8liSM/MB6BLqz4yhbekcGmByOpErp7JkRypLUlcVFJfwzo8H+c/PpaNMDbxdeWVEBwa1DzI7mlyhrUfSeWnpPnYczQCgib8H0wZHMLRjY625JbWGypIdqSxJXbfzWAZPLtrB/tTS1ZqHnxllCtAok8M5mp7HrOUxLNuZDICXqxOP3NCSiX3DcXfRzW6ldlFZsiOVJZHSUaZ/rD7A3J/jsBnQwNuN10a2Z0A7jTI5guz8Iv79Uxzvrz9MYbENiwVu7xbCEwNa68bKUmupLNmRypLI/9t+tHSU6ew9wUZcFcwLw9vh76lRppqoxGbwxZajzFkZy4mcQgB6t6jPsze3pW2wfp9J7aayZEcqSyLl5ReV8NbqA7z3S+koU6CPGzNHdqB/20ZmR5PfiTp4gpeX7i1bCiK8gRdPD4mkf2RDnZckdYLKkh2pLImc37aEUzy5aAdxx3MBuLVzE54f1g4/T90rzEyHjufw2vf7WL0vDQBfd2f+2r8143o2w9VZN7uVukNlyY5UlkQuLL+ohL+v2s9/1x3CZkAjXzdm3tqBGyM0ymRvGXmF/OPHA3wUfYRim4GT1cK4ns34a79WOhlf6iSVJTtSWRK5uK1HTjFl0Q4OnSgdZRrVpSnPDWurO9LbQVGJjY9/PcJbqw+QeboIgBsjGvL0kEhaNvQ2OZ2IeVSW7EhlSeTS5BeVMGdlLP9bfxjjzCjTrFs7ckNEQ7Oj1UqGYfDjvjRe+35fWUlt08iHZ4dGck2rQJPTiZhPZcmOVJZELs+W+HSmfLmTw2f+gI/u2pRnh2qUqSrtS87ilWV7iTp4EoAG3q48cVMbxnRrirOTzksSAZUlu1JZErl8pwtLmL0ylg+iSkeZgnzdmTWqA9e30SjTlTieXcDfVsXy+eaj2AxwdbJyX99w/nxDC3zcVUZFfk9lyY5UlkQqb3N8OlMW7SD+ZB5QuhDiM0Mj8dUf9suSX1TCB1GH+ffaOHIKigG4uUNjpg2OIKSep8npRGomlSU7UlkSuTKnC0t4Y0UM8zfEYxjQ2M+d10d15NrWOq/mYgzDYNmuZGb9EMOxU6cB6NjUjxlD23J1WD2T04nUbCpLdqSyJFI1Nh1OZ8qXOzhyZpTpzu4hPD0kUtNHF7DjaAYvL93LliOngNKpzKcGtWHEVU2wWrWopMjFqCzZkcqSSNXJKyzmjeWxzN8QD5Te7X7WqA66eut3kjNP88byWL7elgiAh4sTD13XnAevbY6nq7PJ6UQch8qSHaksiVS9Xw+d5Kkvd5KQXjrKdFePUJ4eEom3W90tA3mFxcz9+RDv/RJHfpENKF2vasrANgT56Wa3IpdLZcmOVJZEqkduQTGvL4/hw+gjQOko0xu3daRPywYmJ7Mvm81g8W/HeHNFLGnZBQB0D6vHjKFt6dDUz+R0Io5LZcmOVJZEqteGuBM89eXOshOY7+4ZyvTBkXjVgVGmjYdO8vKyvexOzAIgpJ4HTw+OZFD7IN3sVuQKqSzZkcqSSPXLLShm5g/7+PjXBACaBpSOMvVuUTtHmY6czGXm9zEs35MCgI+bM5NubMn4PmG4OTuZnE6kdlBZsiOVJRH72XDwBFO+3EliRuko0z29mjF1UEStGWXKyi/in2sOMj8qnsISG1YL3Nk9lMdvak0Dbzez44nUKipLdqSyJGJfOQXFvPb9Pj7ZWDrKFFLPgzdv60TP5vVNTlZ5xSU2Pt18lL+v2k96biEA17RqwLM3t6VNkI/J6URqJ5UlO1JZEjHH+gMnmLr4/0eZ7u3VjKmDIxzu8vmf9x/n1WV72Z+aA0CLQC+evbkt17cJ1HlJItVIZcmOVJZEzJOdX8Rr38fw6abSUabQep68eVtHejjAKNPBtGxeWbaPn2KPA+Dv6cLj/VtzV49QXHSzW5Fqp7JkRypLIub7Zf9xpi3eSVJmPhYL3NsrjKcGtamRo0zpuYW8tXo/CzcmUGIzcHGycE+vMP5yYyv8PLVauYi9qCzZkcqSSM2QlV/Ea8v28dnmowCE1ffkzdGdasw90gqLbXwYHc8/fjxAdn7pzW5vatuIp4dEEt7Ay+R0InWPypIdqSyJ1Cw/xaYx/atdJJ8ZZZrQO5wpA9vg4WrOJfeGYbBybyozv99H/Jn73kU29mXG0Mhau/SBiCNQWbIjlSWRmicrv4hXlu7liy3HAAhv4MXs0R3p2sy+o0x7kjJ5eelefj2UDkADbzemDGzNbV1DcNLNbkVMpbJkRypLIjXX2pg0pn21k9SsAiwWmNgnnCcHtsHdpXpHmdKy8pm9MpZFW49hGODmbOWBa5rz8PUt6vT97URqEpUlO1JZEqnZMk8X8fLSvXy5tXSUqXkDL94c3YmuzQKq/HvlF5Xwv3WH+PdPceQVlgAwvFMwUwdH0MTfo8q/n4hUnsqSHaksiTiGNTGpTFu8i7TsAqwWuP+a5jxxU+sqGWUyDIMlO5J4/YcYkjLzAegc6s+MoW3pElr1pUxErpzKkh2pLIk4jsy8Il5cuoevfksESheAfHN0pysqNFuPnOLlpXvZfjQDgGA/d6YOjmB4p2AtKilSg6ks2ZHKkojjWb03lelf7+L4mVGmB65tzuP9L2+U6dipPF5fHst3O5IA8HR14pHrW3D/Nc2r/ZwoEblyKkt2pLIk4pgy8gp58bu9fL2tdJSpZUNvZo/uxFUh/hXul1NQzH9+Osj/1h2moNiGxQKjuzblyQFtaOjrbofkIlIVVJbsSGVJxLGt3JPC01/v5kRO6SjTQ9e14LH+rXBzLj86VGIz+HLrUWav3M/x7AIAejavx7M3t6V9Ez8zoovIFVBZsiOVJRHHdyq3kBe+28O320un1FqdGWXqdGaUaUPcCV5Zuo+9yVlA6ergTw+J5Ka2jXRekoiDUlmyI5Ulkdpj+e4Unv1mFydyCnGyWri/bziHTuSyam8qAL7uzvylXyvu6RWGq7NudiviyFSW7EhlSaR2Sc8t5Pkle8pO3AZwslq4u0cof+3fmnperiamE5Gqcql/v7WMrIjIH9TzcuWdOztzc4cgXvs+htaNvJk2OIKWDX3MjiYiJlBZEhG5gEHtGzOofWOzY4iIyTThLiIiIlIBlSURERGRCqgsiYiIiFRAZUlERESkAipLIiIiIhVQWRIRERGpgMOUpfT0dMaOHYuvry/+/v5MnDiRnJycCveJi4tj5MiRBAYG4uvry5gxY0hNTT3vawsKCrjqqquwWCxs3769Gj6BiIiIOCKHKUtjx45lz549rFq1iqVLl/LLL7/w4IMPXvD1ubm5DBgwAIvFwpo1a4iKiqKwsJBhw4Zhs9nOef1TTz1FcHBwdX4EERERcUAOsSjlvn37WL58OZs3b6Zbt24AvPPOOwwZMoTZs2eft+RERUURHx/Ptm3bypYwX7BgAQEBAaxZs4b+/fuXvfaHH35g5cqVLF68mB9++ME+H0pEREQcgkOMLEVHR+Pv719WlAD69++P1Wpl48aN592noKAAi8WCm5tb2TZ3d3esVivr168v25aamsoDDzzARx99hKenZ/V9CBEREXFIDlGWUlJSaNiwYbltzs7O1KtXj5SUlPPu07NnT7y8vJg6dSp5eXnk5uby5JNPUlJSQnJyMgCGYTB+/HgefvjhckXsYgoKCsjKyir3EBERkdrJ1LI0bdo0LBZLhY+YmJhKvXdgYCCLFi3iu+++w9vbGz8/PzIyMujSpQtWa+nHfuedd8jOzmb69OmX9d4zZ87Ez8+v7BESElKpjCIiIlLzmXrO0uTJkxk/fnyFr2nevDlBQUGkpaWV215cXEx6ejpBQUEX3HfAgAHExcVx4sQJnJ2d8ff3JygoiObNmwOwZs0aoqOjy03VAXTr1o2xY8eyYMGC877v9OnTeeKJJ8q+zsrKUmESERGppUwtS4GBgQQGBl70db169SIjI4OtW7fStWtXoLTo2Gw2evTocdH9GzRoULZPWloaw4cPB+Dtt9/mlVdeKXtdUlISAwcO5PPPP6/wfd3c3M4pWCIiIlI7OcTVcJGRkQwaNIgHHniAuXPnUlRUxKRJk7jjjjvKroRLTEykX79+fPjhh3Tv3h2AefPmERkZSWBgINHR0fz1r3/l8ccfp02bNgCEhoaW+z7e3t4AtGjRgqZNm15yPsMwAHTukoiIiAM5+3f77N/xC3GIsgSwcOFCJk2aRL9+/bBarYwaNYq333677PmioiJiY2PJy8sr2xYbG8v06dNJT08nLCyMZ555hscff7zKs2VnZwNoKk5ERMQBZWdn4+fnd8HnLcbF6pRclM1mIykpCR8fHywWi9lxys6hOnr0aNkaU3WZjkd5Oh7l6XicS8ekPB2P8mrT8TAMg+zsbIKDg8su/jofhxlZqsmsVutlTdvZi6+vr8P/IFclHY/ydDzK0/E4l45JeToe5dWW41HRiNJZDrHOkoiIiIhZVJZEREREKqCyVAu5ubnx/PPPa3mDM3Q8ytPxKE/H41w6JuXpeJRXF4+HTvAWERERqYBGlkREREQqoLIkIiIiUgGVJREREZEKqCw5sMTERO6++27q16+Ph4cHHTp0YMuWLWXPG4bBc889R+PGjfHw8KB///4cOHDAxMTVq6LjUVRUxNSpU+nQoQNeXl4EBwdzzz33kJSUZHLq6nWxn5Hfe/jhh7FYLLz11lv2DWlHl3I89u3bx/Dhw/Hz88PLy4urr76ahIQEkxJXr4sdj5ycHCZNmkTTpk3x8PCgbdu2zJ0718TE1ScsLAyLxXLO489//jMA+fn5/PnPf6Z+/fp4e3szatQoUlNTTU5dfSo6Hunp6Tz66KO0adMGDw8PQkND+ctf/kJmZqbZsauNFqV0UKdOnaJPnz7ccMMN/PDDDwQGBnLgwAECAgLKXvPGG2/w9ttvs2DBAsLDw5kxYwYDBw5k7969uLu7m5i+6l3seOTl5fHbb78xY8YMOnXqxKlTp/jrX//K8OHDL1geHN2l/Iyc9fXXX/Prr7+W3WuxNrqU4xEXF0ffvn2ZOHEiL774Ir6+vuzZs6fW/fcCl3Y8nnjiCdasWcPHH39MWFgYK1eu5JFHHiE4OLjshuS1xebNmykpKSn7evfu3dx0002MHj0agMcff5xly5axaNEi/Pz8mDRpErfeeitRUVFmRa5WFR2PpKQkkpKSmD17Nm3btuXIkSM8/PDDJCUl8eWXX5qYuhoZ4pCmTp1q9O3b94LP22w2IygoyHjzzTfLtmVkZBhubm7Gp59+ao+IdnWx43E+mzZtMgDjyJEj1ZTKXJd6TI4dO2Y0adLE2L17t9GsWTPj73//e/WHM8GlHI/bb7/duPvuu+2UyFyXcjzatWtnvPTSS+W2denSxXjmmWeqM1qN8Ne//tVo0aKFYbPZjIyMDMPFxcVYtGhR2fP79u0zACM6OtrElPbz++NxPl988YXh6upqFBUV2TmZfWgazkEtWbKEbt26MXr0aBo2bEjnzp3573//W/b84cOHSUlJoX///mXb/Pz86NGjB9HR0WZErlYXOx7nk5mZicViwd/f3z4h7exSjonNZmPcuHFMmTKFdu3amZTUPi52PGw2G8uWLaN169YMHDiQhg0b0qNHD7755hvzQlejS/n56N27N0uWLCExMRHDMFi7di379+9nwIABJqW2j8LCQj7++GPuu+8+LBYLW7dupaioqNzv04iICEJDQ2vl79M/+uPxOJ/MzEx8fX1xdq6lE1ZmtzWpHDc3N8PNzc2YPn268dtvvxnvvvuu4e7ubsyfP98wDMOIiooyACMpKancfqNHjzbGjBljRuRqdbHj8UenT582unTpYtx11112Tmo/l3JMXnvtNeOmm24q+9dibR5ZutjxSE5ONgDD09PT+Nvf/mZs27bNmDlzpmGxWIyffvrJ5PRV71J+PvLz84177rnHAAxnZ2fD1dXVWLBggYmp7ePzzz83nJycjMTERMMwDGPhwoWGq6vrOa+7+uqrjaeeesre8ezuj8fjj44fP26EhoYaTz/9tJ2T2Y/KkoNycXExevXqVW7bo48+avTs2dMwjLpXli52PH6vsLDQGDZsmNG5c2cjMzPTXhHt7mLHZMuWLUajRo3K/QKszWXpYscjMTHRAIw777yz3GuGDRtm3HHHHXbLaS+X8t/Mm2++abRu3dpYsmSJsWPHDuOdd94xvL29jVWrVtk7rl0NGDDAGDp0aNnXdb0s/fF4/F5mZqbRvXt3Y9CgQUZhYaGdk9mPpuEcVOPGjWnbtm25bZGRkWVX7QQFBQGcc7VGampq2XO1ycWOx1lFRUWMGTOGI0eOsGrVqlpxx+wLudgxWbduHWlpaYSGhuLs7IyzszNHjhxh8uTJhIWFmZC4el3seDRo0ABnZ+dL+jmqDS52PE6fPs3TTz/N3/72N4YNG0bHjh2ZNGkSt99+O7NnzzYjsl0cOXKE1atXc//995dtCwoKorCwkIyMjHKvra2/T3/vfMfjrOzsbAYNGoSPjw9ff/01Li4uJiS0D5UlB9WnTx9iY2PLbdu/fz/NmjUDIDw8nKCgIH788cey57Oysti4cSO9evWya1Z7uNjxgP8vSgcOHGD16tXUr1/f3jHt6mLHZNy4cezcuZPt27eXPYKDg5kyZQorVqwwI3K1utjxcHV15eqrr77oz1FtcbHjUVRURFFREVZr+T8TTk5O2Gw2u+W0t3nz5tGwYUNuvvnmsm1du3bFxcWl3O/T2NhYEhISauXv09873/GA0r8nAwYMwNXVlSVLltTKK0bLMXtoSypn06ZNhrOzs/Hqq68aBw4cMBYuXGh4enoaH3/8cdlrZs2aZfj7+xvffvutsXPnTuOWW24xwsPDjdOnT5uYvHpc7HgUFhYaw4cPN5o2bWps377dSE5OLnsUFBSYnL56XMrPyB/V5mm4SzkeX331leHi4mK89957xoEDB4x33nnHcHJyMtatW2di8upxKcfjuuuuM9q1a2esXbvWOHTokDFv3jzD3d3d+Pe//21i8upTUlJihIaGGlOnTj3nuYcfftgIDQ011qxZY2zZssXo1avXOdOYtc2FjkdmZqbRo0cPo0OHDsbBgwfL/T4tLi42KW31UllyYN99953Rvn17w83NzYiIiDDee++9cs/bbDZjxowZRqNGjQw3NzejX79+RmxsrElpq19Fx+Pw4cMGcN7H2rVrzQtdzS72M/JHtbksGcalHY/333/faNmypeHu7m506tTJ+Oabb0xIah8XOx7JycnG+PH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" ] }, "metadata": {}, @@ -648,12 +340,7 @@ } ], "source": [ - "from IPython.display import Image, display\n", - "\n", - "plot_task = [t for t in wg.tasks if t.name.startswith('plot_energy_volume_curve')][0]\n", - "plot_file = f\"{plot_task.node.get_remote_workdir()}/evcurve.png\"\n", - "\n", - "display(Image(filename=str(plot_file)))\n" + "wg.run()" ] }, { @@ -665,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -674,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -683,18 +370,18 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a850e49d3d5c4eddb02cdff71ddd2065", + "model_id": "4caef04a260d4db6a145bd9ce8cea6f8", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "0it [00:00, 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was saved and received the ID: 8\n", - "The job get_dict_1e47509b88d63a21fd421686554c8f4a was saved and received the ID: 9\n", - "The job calculate_qe_e3c0bf43f7edf24d215901bf93271e87 was saved and received the ID: 10\n" + "The job get_bulk_structure_f1e730ed97e30e5439e855d2ac41396f was saved and received the ID: 4\n", + "The job get_dict_bbed7e528d369f5fa02591748be3166d was saved and received the ID: 5\n", + "The job calculate_qe_a8254d13768b11f13ee9368124819d22 was saved and received the ID: 6\n" ] }, { @@ -1466,9 +1153,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job generate_structures_6326b8ea2e84f099ea95fec76459bc56 was saved and received the ID: 11\n", - "The job get_dict_27e8162e8ea354dae951497ba9e7b9dd was saved and received the ID: 12\n", - "The job calculate_qe_1bf9ad9fb363acc8d268f1e80ed53382 was saved and received the ID: 13\n" + "The job generate_structures_7c00a7a36fdf7a83905a933d6458c9f6 was saved and received the ID: 7\n", + "The job get_dict_9fea1887c87374cda848be8c394400b6 was saved and received the ID: 8\n", + "The job calculate_qe_a2a0f9b449a97089f66fd4697c1184d4 was saved and received the ID: 9\n" ] }, { @@ -1482,8 +1169,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_3e0ebfe87008f5e62291452793644d90 was saved and received the ID: 14\n", - "The job calculate_qe_b6c10ce0f5581c5163295b4ddf908191 was saved and received the ID: 15\n" + "The job get_dict_736160eef13d04e2065cf2c38c62bf47 was saved and received the ID: 10\n", + "The job calculate_qe_42a1041b6acc70e91f55e4a2a877ec0b was saved and received the ID: 11\n" ] }, { @@ -1497,8 +1184,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_e59211242612ae95433521ef6b374e6f was saved and received the ID: 16\n", - "The job calculate_qe_5be20ee465cbcc51f54d4167a9f081ae was saved and received the ID: 17\n" + "The job get_dict_618fccf949c159d8a1573c4cda125aae was saved and received the ID: 12\n", + "The job calculate_qe_d5f2ff242c26f0b8badef6df9a0504a5 was saved and received the ID: 13\n" ] }, { @@ -1512,8 +1199,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_9dd0bb1e6343ccf9acbdb1bea11bd506 was saved and received the ID: 18\n", - "The job calculate_qe_0e5300454c9d12e36c6d2925c2e6d859 was saved and received the ID: 19\n" + "The job get_dict_61f81e04658f67188ad4502e007fe475 was saved and received the ID: 14\n", + "The job calculate_qe_2f09fb4c3a6570dbdc141588f072fec6 was saved and received the ID: 15\n" ] }, { @@ -1527,8 +1214,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_da6f93cd2ef0e45bdf569b708131d0a6 was saved and received the ID: 20\n", - "The job calculate_qe_326496dc7db0f79fde4d7e1abe2eedf3 was saved and received the ID: 21\n" + "The job get_dict_0bf0210bb59731567645c21e9c07d901 was saved and received the ID: 16\n", + "The job calculate_qe_8e0081288044bb59e2eb975aab60c98c was saved and received the ID: 17\n" ] }, { @@ -1542,14 +1229,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_list_09ab4d5239e439dc9eb2292db9516c04 was saved and received the ID: 22\n", - "The job get_list_30bb97099893030717e722ed661022c7 was saved and received the ID: 23\n", - "The job plot_energy_volume_curve_e82d98c6a163e43fe8943f89cb94dee9 was saved and received the ID: 24\n" + "The job get_list_56b7ebcd322ece2c1200ba8d040fc0bd was saved and received the ID: 18\n", + "The job get_list_8d6e79fedf35b08653113905df993b7b was saved and received the ID: 19\n", + "The job plot_energy_volume_curve_fa6e68740c464fa9316d532209ff492f was saved and received the ID: 20\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1572,7 +1259,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "aiida", "language": "python", "name": "python3" }, @@ -1586,7 +1273,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.5" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/pyiron_base_qe.ipynb b/pyiron_base_qe.ipynb index 59f6206..13e61e4 100644 --- a/pyiron_base_qe.ipynb +++ b/pyiron_base_qe.ipynb @@ -85,12 +85,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6668170c6044029acd21bbd6c02619a", + "model_id": "cc655495af0844928dc62f4f138acd31", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "0it [00:00, ?it/s]" + " 0%| | 0/17 [00:00" + "Profile" ] }, "execution_count": 14, @@ -279,359 +279,61 @@ "name": "stderr", "output_type": "stream", "text": [ - "03/22/2025 05:45:47 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: pickle_node18,pickle_node19,pickle_node20,pickle_node21,pickle_node22,pickle_node23,pickle_node24,pickle_node25,pickle_node26,pickle_node27,pickle_node28,pickle_node29,pickle_node30,pickle_node31,pickle_node32\n", - "03/22/2025 05:46:07 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 712, 719, 726, 733, 740, 747, 754, 761, 768, 775, 782, 789, 796, 803, 810\n", - "03/22/2025 05:47:16 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node18, type: PYTHONJOB, finished.\n", - "03/22/2025 05:47:17 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node19, type: PYTHONJOB, finished.\n" + "04/01/2025 10:56:19 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_bulk_structure8\n", + "04/01/2025 10:56:20 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: get_bulk_structure8, type: PyFunction, finished.\n", + "04/01/2025 10:56:21 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict7\n", + "04/01/2025 10:56:22 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: get_dict7, type: PyFunction, finished.\n", + "04/01/2025 10:56:22 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe6\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:56:41 AM <3368622> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['energy', 'volume'])\n", + "04/01/2025 10:56:42 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: calculate_qe6, type: PyFunction, finished.\n", + "04/01/2025 10:56:42 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: generate_structures5\n", + "04/01/2025 10:56:44 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: generate_structures5, type: PyFunction, finished.\n", + "04/01/2025 10:56:44 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict4,get_dict10,get_dict12,get_dict14,get_dict16\n", + "04/01/2025 10:56:45 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: get_dict4, type: PyFunction, finished.\n", + "04/01/2025 10:56:46 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict10,get_dict12,get_dict14,get_dict16,calculate_qe3\n", + "04/01/2025 10:56:47 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: get_dict10, type: PyFunction, finished.\n", + "04/01/2025 10:56:48 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict12,get_dict14,get_dict16,calculate_qe3,calculate_qe9\n", + "04/01/2025 10:56:49 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: get_dict12, type: PyFunction, finished.\n", + "04/01/2025 10:56:49 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict14,get_dict16,calculate_qe3,calculate_qe9,calculate_qe11\n", + "04/01/2025 10:56:50 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: get_dict14, type: PyFunction, finished.\n", + "04/01/2025 10:56:51 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict16,calculate_qe3,calculate_qe9,calculate_qe11,calculate_qe13\n", + "04/01/2025 10:56:52 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: get_dict16, type: PyFunction, finished.\n", + "04/01/2025 10:56:53 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe3,calculate_qe9,calculate_qe11,calculate_qe13,calculate_qe15\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:56:57 AM <3368622> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:56:57 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: calculate_qe3, type: PyFunction, finished.\n", + "04/01/2025 10:56:58 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe9,calculate_qe11,calculate_qe13,calculate_qe15\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:57:02 AM <3368622> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:57:02 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: calculate_qe9, type: PyFunction, finished.\n", + "04/01/2025 10:57:03 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe11,calculate_qe13,calculate_qe15\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:57:07 AM <3368622> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:57:08 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: calculate_qe11, type: PyFunction, finished.\n", + "04/01/2025 10:57:08 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe13,calculate_qe15\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:57:14 AM <3368622> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:57:14 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: calculate_qe13, type: PyFunction, finished.\n", + "04/01/2025 10:57:15 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe15\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:57:20 AM <3368622> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:57:20 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: calculate_qe15, type: PyFunction, finished.\n", + "04/01/2025 10:57:21 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list2,get_list17\n", + "04/01/2025 10:57:22 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: get_list2, type: PyFunction, finished.\n", + "04/01/2025 10:57:23 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list17\n", + "04/01/2025 10:57:24 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: get_list17, type: PyFunction, finished.\n", + "04/01/2025 10:57:25 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: plot_energy_volume_curve1\n", + "04/01/2025 10:57:26 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|update_task_state]: Task: plot_energy_volume_curve1, type: PyFunction, finished.\n", + "04/01/2025 10:57:26 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", + "04/01/2025 10:57:28 AM <3368622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51282|WorkGraphEngine|finalize]: Finalize workgraph.\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:17 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node20, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:18 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node21, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:18 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node22, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:19 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node23, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:19 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node24, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:20 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node25, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:20 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node26, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:21 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node27, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:21 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node28, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:22 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node29, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:22 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node30, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:23 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node31, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:23 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: pickle_node32, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:47:28 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_bulk_structure8\n", - "03/22/2025 05:47:31 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 877\n", - "03/22/2025 05:47:42 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: get_bulk_structure8, type: PYTHONJOB, finished.\n", - "03/22/2025 05:47:48 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict7\n", - "03/22/2025 05:47:51 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 888\n", - "03/22/2025 05:48:02 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: get_dict7, type: PYTHONJOB, finished.\n", - "03/22/2025 05:48:07 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe6\n", - "03/22/2025 05:48:10 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 899\n", - "03/22/2025 05:48:47 PM <166198> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['energy', 'volume'])\n", - "03/22/2025 05:48:48 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: calculate_qe6, type: PYTHONJOB, finished.\n", - "03/22/2025 05:48:53 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: generate_structures5\n", - "03/22/2025 05:48:56 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 910\n", - "03/22/2025 05:49:07 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: generate_structures5, type: PYTHONJOB, finished.\n", - "03/22/2025 05:49:12 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict4,get_dict10,get_dict12,get_dict14,get_dict16\n", - "03/22/2025 05:49:21 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 925, 932, 939, 946, 953\n", - "03/22/2025 05:49:48 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: get_dict4, type: PYTHONJOB, finished.\n", - "03/22/2025 05:49:49 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: get_dict10, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:49:49 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: get_dict12, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:49:50 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: get_dict14, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:49:51 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: get_dict16, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:49:56 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe3,calculate_qe9,calculate_qe11,calculate_qe13,calculate_qe15\n", - "03/22/2025 05:50:04 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 980, 987, 994, 1001, 1008\n", - "03/22/2025 05:50:33 PM <166198> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:50:34 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: calculate_qe3, type: PYTHONJOB, finished.\n", - "03/22/2025 05:50:39 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "03/22/2025 05:50:41 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 987, 994, 1001, 1008\n", - "03/22/2025 05:50:49 PM <166198> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:50:50 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: calculate_qe9, type: PYTHONJOB, finished.\n", - "03/22/2025 05:50:57 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "03/22/2025 05:50:59 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 994, 1001, 1008\n", - "03/22/2025 05:51:08 PM <166198> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:51:08 PM <166198> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:51:09 PM <166198> aiida.parser.PythonJobParser: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "03/22/2025 05:51:09 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: calculate_qe11, type: PYTHONJOB, finished.\n", - "03/22/2025 05:51:10 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: calculate_qe13, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:51:10 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: calculate_qe15, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:51:15 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list2,get_list17\n", - "03/22/2025 05:51:20 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1040, 1047\n", - "03/22/2025 05:51:35 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: get_list2, type: PYTHONJOB, finished.\n", - "03/22/2025 05:51:36 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: get_list17, type: PYTHONJOB, finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "invalid state\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "03/22/2025 05:51:41 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: plot_energy_volume_curve1\n", - "03/22/2025 05:51:44 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 1062\n", - "03/22/2025 05:51:55 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|update_task_state]: Task: plot_energy_volume_curve1, type: PYTHONJOB, finished.\n", - "03/22/2025 05:52:00 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "03/22/2025 05:52:02 PM <166198> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [705|WorkGraphEngine|finalize]: Finalize workgraph.\n" - ] - } - ], - "source": [ - "result = wg.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6a8d1a17-3698-4873-8937-616e9e7dc7ca", - "metadata": {}, - "outputs": [ { "data": { - "image/png": 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paWlV2Q2mGHv684EXbskg05ZvbhgRERExhYcZv3T8+PHcdtttF9wmPDyczZs3k5V19mfYHj58mKCgoHO+7tSpUzz99NPMnz+f4cOHA9C5c2cSExN57bXXGDhwIM2alS6MHB0dXe61UVFRpKaef5kUb29vvL29L5jb0XVsbqVXeGPWpBzjs4T9PDHk3NdRioiISN1lSgEMCAggICDgotvFxMRgs9lYs2YNvXr1AmD16tXYbDb69u17ztcUFRVRVFR01t287u7uZSN/4eHhhISEkJSUVG6b5ORkhg0bdilvyamM6xfOmpRjfLEmlfFXt8XH093sSCIiIlKLHPoawKioKIYOHcr9999PQkICCQkJ3H///YwYMaLcHcCRkZHMnz8fAH9/f6688komTpzI8uXL2bdvHzNnzuSTTz7hxhtvBEqnlSdOnMjbb7/NnDlz2L17N5MnT2bnzp3ce++9przX2jQoOojmDetxLLeQBYnpZscRERGRWmbKCGBlfP755zz88MMMHjwYgJEjR/LOO++U2yYpKQmbzVb29axZs5g0aRJ33nknx44dIywsjJdeeokHH3ywbJtHH32U/Px8JkyYwLFjx+jSpQuLFy+mTZs2tfPGTOTh7kZsTBjTFu7ko5X7GN2zxQWvqRQREZG6xaHXAXR0zryO0PG8QvpM/Zn8Ijuz/tSHPq2bmB1JRESkVjjz+bu6OPQUsNSchr5e3NS9BQAzVu4zOY2IiIjUJhVAF3ZmSZjF27NIO5ZnbhgRERGpNSqALqx9UAP6tw3AbsCnCfvNjiMiIiK1RAXQxY3rFw7ArDWp5BUWmxtGREREaoUKoIu7KiKQsCa+5OQXM3fDQbPjiIiISC1QAXRxbm4W7o4JB2Dmyn3Y7bopXEREpK5TARRG92xBfW8P9hzOZcXuI2bHERERkRqmAig08PFkVA8tCSMiIuIqVAAFKF0SxmKBZUmH2Xv4pNlxREREpAapAAoA4QF+XBURCMDHq1LMDSMiIiI1SgVQypxZEmbO+gPk5BeZG0ZERERqjAqglOnfNoB2gfXJLSxh9roDZscRERGRGqICKGUsFgtjT48CfrwqhRItCSMiIlInqQBKOTd2a46/jwepx/JYuvOQ2XFERESkBqgASjm+Xh7c3qslADNXaUkYERGRukgFUM4SGxOGmwVW7j5KUuYJs+OIiIhINVMBlLO0aOTLkA7BgEYBRURE6iIVQDmnsX3DAZi34SDZuYXmhhEREZFqpQIo59SrVWOim/lTUGxn1to0s+OIiIhINVIBlHOyWCxlC0N/Gp9CcYnd3EAiIiJSbVQA5byu6xJCEz8v0m35LNqWZXYcERERqSYqgHJePp7u3NG7dEmYGSt1M4iIiEhdoQIoF3RXnzA83Cys25/NlgM2s+OIiIhINVABlAsK8vdheOdmAMzQkjAiIiJ1ggqgXNS4fq0A+H5TBodPFJicRkRERKpKBVAuqmtoQ7qGNqSwxM7nq/ebHUdERESqSAVQKuTMkjCfJaRSWKwlYURERJyZCqBUyLWdmhHk782RkwX8sCXd7DgiIiJSBSqAUiGe7m7E9gkDYMbKFAzDMDmRiIiIXCoVQKmw23u1xMvDjc0HbGxIPW52HBEREblEKoBSYU3qe3N9lxBAC0OLiIg4MxVAqZQzS8Is3JpJhu2UyWlERETkUqgASqVEh/jTu1VjSuwGn8ZrSRgRERFnpAIolXZmSZgv16SSX1RibhgRERGpNBVAqbRB0cE0b1iP7Lwivk08aHYcERERqSQVQKk0dzcLd/fVkjAiIiLOSgVQLsmtPVtSz9OdnZkniN971Ow4IiIiUgkqgHJJrL6e3NS9OQAzV6aYG0ZEREQqRQVQLtmZm0EW78gi7VieuWFERESkwlQA5ZK1DWzA5e0CMAz4eFWK2XFERESkglQApUruOb0w9Ffr0sgtKDY5jYiIiFSECqBUyZXtm9IqwI8T+cXM23DA7DgiIiJSASqAUiVubhbujjm9JMyqFOx2LQkjIiLVw3aqyOwIdZYKoFTZqJ6h1Pf2YO/hXH7dddjsOCIiUgf8sDmDK6cvY8GmdLOj1EkqgFJl9b09GN2zBVC6MLSIiEhVHDlZwORvt3I8r4jdWSfMjlMnqQBKtRjbNxyLBX5JPsyewyfNjiMiIk7s2W+3ciy3kMjgBoy/up3ZceokFUCpFmFN/LgmMhDQkjAiInLpvt+czo9bMvFws/Da6C54eaiq1ATtVak2404vCTNn/QFduCsiIpV2+EQBk7/ZCsBfr2pLx+ZWkxPVXSqAUm36tmlC+6D65BWWMHtdmtlxRETEiRiGwTPfbCE7r4ioZv789aq2Zkeq01QApdpYLBbG9i0dBfw4PoUSLQkjIiIVtGBTOou2ZeHhZuF1Tf3WOO1dqVY3dmtOQ19P0o6d4ucdWWbHERERJ3DoRD7PLdgGwENXtyM6xN/kRHWfCqBUq3pe7tx2WUtAS8KIiMjFGYbB3+eXLvnSIcSfv1zVxuxILkEFUKpdbEwY7m4W4vceZUdGjtlxRETEgX2bmM7i7Vl4ult4/ZYueLqrmtQG7WWpds0b1mNIhyBAS8KIiMj5Hcr539Tvw1e3IzJYU7+1RQVQasSZJWHmbzzIsdxCk9OIiIijMQyDp+dvwXaqiE7NrTw4QFO/tUkFUGpEz7BGdGzuT0GxnS/XpJodR0REHMz8jQdZsuMQXu5uvDZaU7+1TXtbasTvl4T5LGE/RSV2kxOJiIijyMrJZ8rpqd9HBrYjIriByYlcjwqg1JjrujQjoL4XGbZ8Fm3LNDuOiIg4AMMwmDRvCzn5xXRuYeWBK1qbHcklqQBKjfH2cOeO3mGAloQREZFSczccZOnO0qnf10d3wUNTv6bQXpcadVfvlni6W1i/P5vNB46bHUdEREyUacvn+e9Kp34nDGpPuyBN/ZpFBVBqVKC/D8M7NQNgpkYBRURclmEY/G3eZk7kF9MltCH3X97K7EguTQVQatyZJWG+25zOoRP5JqcREREzzF5/gOVJh/HycOP10Z019Wsy7X2pcV1CG9K9ZUOKSgw+T9CSMCIiribDdooXvtsOwOOD2tM2UFO/ZnP4ApidnU1sbCxWqxWr1UpsbCzHjx+/4GtOnjzJ+PHjadGiBfXq1SMqKor33nuv3DaZmZnExsYSHByMn58f3bt3Z86cOTX4TlzbmVHAz1fvp6C4xOQ0IiJSWwzD4Km5WzhRUEy3lg2573Ld9esIHL4A3nHHHSQmJhIXF0dcXByJiYnExsZe8DUTJkwgLi6Ozz77jB07djBhwgQeeughvv3227JtYmNjSUpKYsGCBWzZsoWbbrqJW2+9lY0bN9b0W3JJQzsGE+zvw5GThfywOcPsOCIiUku+XpfGr8mlU7/TR3XB3c1idiTBwQvgjh07iIuL47///S8xMTHExMTwwQcf8P3335OUlHTe18XHx3P33XczYMAAwsPD+dOf/kSXLl1Yt25duW0eeughevXqRevWrXnmmWdo2LAhGzZsqI235nI83d2IjfnfkjCGYZicSEREatrB46d44fsdAEwcHEHbwPomJ5IzHLoAxsfHY7Va6d27d9lzffr0wWq1smrVqvO+rn///ixYsICDBw9iGAbLli0jOTmZIUOGlNvmq6++4tixY9jtdmbNmkVBQQEDBgw4788tKCggJyen3EMq7vZeLfH2cGPLQRvr92ebHUdERGqQYRj8be5mThYU0yOsEff0112/jsShC2BmZiaBgYFnPR8YGEhm5vk/WeLtt98mOjqaFi1a4OXlxdChQ3n33Xfp379/2TZfffUVxcXFNGnSBG9vbx544AHmz59Pmzbn/zDqqVOnll2LaLVaCQ0NrdobdDGN/by4oWtzQAtDi4jUdbPWpvHbriN4e7gxfVRnTf06GFMK4JQpU7BYLBd8nJmutVjOPmAMwzjn82e8/fbbJCQksGDBAtavX8/rr7/OX/7yF5YsWVK2zTPPPEN2djZLlixh3bp1PPbYY4wePZotW7ac9+dOmjQJm81W9khLS6vCXnBNY/uFAxC3LZP046fMDSMiIjXiQHYeL35fetfvxCERtG6qqV9HYzFMuBjryJEjHDly5ILbhIeH88UXX/DYY4+ddddvw4YNefPNNxk3btxZrzt16hRWq5X58+czfPjwsufvu+8+Dhw4QFxcHHv27KFt27Zs3bqVDh06lG0zcOBA2rZty7///e8KvY+cnBysVis2mw1/f/8KvUbgtv/Ek7D3GH8e0IanhkaaHUdERKqRYRjEfriGFbuP0DOsEV89EONwo386f4OHGb80ICCAgICAi24XExODzWZjzZo19OrVC4DVq1djs9no27fvOV9TVFREUVERbm7lBzfd3d2x2+0A5OXlAVxwG6k54/q1ImHvMb5ck8rDV7ejnpe72ZFERKSafLEmlRW7j+Dj6cb00brr11E59DWAUVFRDB06lPvvv5+EhAQSEhK4//77GTFiBBEREWXbRUZGMn/+fAD8/f258sormThxIsuXL2ffvn3MnDmTTz75hBtvvLFs+7Zt2/LAAw+wZs0a9uzZw+uvv87ixYu54YYbzHirLmVgVBAtGtXjeF4R3yQeNDuOiIhUk7Rjebz8Q+ldv08OiaRVgJ/JieR8HLoAAnz++ed06tSJwYMHM3jwYDp37synn35abpukpCRsNlvZ17NmzeKyyy7jzjvvJDo6mmnTpvHSSy/x4IMPAuDp6cmPP/5I06ZNue666+jcuTOffPIJH3/8Mddee22tvj9X5O5m4e6YcKD084G1JIyIiPOz2w2emruZ3MISeoU3ZmzfcLMjyQWYcg1gXaFrCC6d7VQRMVN/Jq+whC/u603fthe/JEBERBzXp/EpTP52G/U83Vn4yOWEO/Don87fTjACKHWTtZ4nN3dvAcBHWhJGRMSppR3LY+rCnQA8NTTCocuflFIBFNPcfXp64OedWew/mmtuGBERuSR2u8HEOZvIKyyhd6vGjDl9iY84NhVAMU3bwPpc0b4phgGfxO83O46IiFyCTxP2k7D3GL5e7kwf1QU33fXrFFQAxVTjTi8M/fXaNE4WFJsbRkREKmX/0VymnZ76/duwSFo28TU5kVSUCqCY6sp2TWkd4MeJgmLmrj9gdhwREamg0qnfzZwqKiGmdRPu6h1mdiSpBBVAMZWbm6Xs4+FmrkrBbtdN6SIizuDj+BTW7Cud+n11VGdN/ToZFUAx3U3dW9DA24N9R3L5Zddhs+OIiMhFpBzJ5ZW40qnfSddGEdpYU7/ORgVQTFff24NbLgsFYIaWhBERcWhn7vrNL7LTr20T7uzV0uxIcglUAMUh3B0TjsUCvyYfZvehk2bHERGR85ixKoW1Kdn4ebnzys2a+nVWKoDiEFo28WVgVBAAM1ftMzmNiIicy97DJ5m+qHTq9+nhUbRopKlfZ6UCKA5j3OmFoeeuP4jtVJG5YUREpJyS03f95hfZ6d82gDs09evUVADFYcS0aUJEUANOFZXw9do0s+OIiMjvzFi5j/X7s6nv7cErozpjsWjq15mpAIrDsFgsZQtDfxyfQomWhBERcQh7Dp9k+qIkAJ4ZHkXzhvVMTiRVVakCmJurz2uVmnVDt+Y09PXkQPYpluzIMjuOiIjLK7EbTJy9iYJiO1e0b8qtp1dtEOdWqQIYFBTEPffcw4oVK2oqj7g4H093bj99XcmMlboZRETEbB+u2MuG1OM08PZg2k2dNPVbR1SqAH755ZfYbDauueYa2rdvz7Rp00hPT6+pbOKiYvuE4e5mIWHvMXZk5JgdR0TEZe0+dILXfkoGYPKIaEI09VtnVKoAXnfddcydO5f09HT+/Oc/8+WXXxIWFsaIESOYN28excXFNZVTXEhIw3oM7RgMaBRQRMQsJXaDJ2ZvprDYzoCIpozu2cLsSFKNLukmkCZNmjBhwgQ2bdrEG2+8wZIlSxg1ahQhISE8++yz5OXlVXdOcTFnloT5JjGdY7mF5oYREXFBH/y2l8S04zTw8WCqpn7rnEsqgJmZmbz66qtERUXxt7/9jVGjRvHzzz/z5ptvMn/+fG644YZqjimupkdYIzo1t1JYbOfLNalmxxERcSm7sk7wxump32dHRNPMqqnfusajMhvPmzePGTNmsGjRIqKjo/nrX//KXXfdRcOGDcu26dq1K926davunOJiziwJ89jXm/g0fj9/uqI1nu5atUhEpKYVl9h5YvYmCkvsXB0ZyKgemvqtiyp1Rh03bhwhISGsXLmSxMRExo8fX678AbRu3Zq///3v1ZlRXNTwzs0IqO9NZk4+C7dmmh1HRMQl/Oe3vWw6YMPfx4OXb9TUb11VqRHAjIwMfH0v/Ll/9erV47nnnqtSKBEAbw937uzdkn/+vIuZK/cxskuI2ZFEROq0pMwTvLV4FwDPXdeBYKuPyYmkplRqBLC4uJicnJyzHidOnKCwUBfqS/W7s09LPN0tbEg9zqa042bHERGps4p+N/V7TWQgN3VvbnYkqUGVKoANGzakUaNGZz0aNmxIvXr1CAsL47nnnsNut9dUXnExgQ18uK5z6cifloQREak57/+yhy0HbVjrefKy7vqt8ypVAGfOnElISAhPP/0033zzDfPnz+fpp5+mefPmvPfee/zpT3/i7bffZtq0aTWVV1zQuH6tAPhhSwaHcvJNTiMiUvfszMzhnz+XTv1OGRlNkL+mfuu6Sl0D+PHHH/P6669zyy23lD03cuRIOnXqxPvvv8/PP/9My5Yteemll3j66aerPay4pk4trPQIa8T6/dl8tjqVxwa1NzuSiEidcWbqt6jEYGBUEDd01dSvK6jUCGB8fPw5l3jp1q0b8fHxAPTv35/UVK3bJtVrXL9wAL5YvZ+C4hJzw4iI1CHvLd/D1oM5NPT15OWbOmrq10VUqgC2aNGCDz/88KznP/zwQ0JDQwE4evQojRo1qp50IqcN6RBMM6sPR04W8t2mDLPjiIjUCTsycvjX0tKp3+dHdiCwgaZ+XUWlpoBfe+01Ro8ezcKFC7nsssuwWCysXbuWnTt3MmfOHADWrl3LrbfeWiNhxXV5ursRGxPGq3FJzFi5j5u7N9e/UkVEqqCoxM7jX5dO/Q6ODtJSWy7GYhiGUZkX7N+/n3//+98kJSVhGAaRkZE88MADhIeH11BEx5WTk4PVasVms+Hv7292nDovO7eQPlN/pqDYzuwHY7gsvLHZkUREnNZbS5J5a8kuGvl68tOEK2nawNvsSLVG5+9KjAAWFRUxePBg3n//faZOnVqTmUTOqZGfFzd2a86stWnMWLlPBVBE5BJtS7fxztLdADx/fUeXKn9SqsLXAHp6erJ161ZNu4mpxp6+GWTRtiwOHj9lbhgRESdUWFw69VtsNxjWMZjrOjczO5KYoFI3gYwZM+acN4GI1JbIYH/6tmlCid3gk/gUs+OIiDidd5btZmfmCRr7efHCDbrr11VV6iaQwsJC/vvf/7J48WJ69uyJn59fue+/8cYb1RpO5FzG9g1n1Z6jzFqTxqPXtKeel7vZkUREnMLWgzbeXVY69fuP6zsQUF9Tv66qUgVw69atdO/eHYDk5ORy39O/IKS2XBMVRGjjeqQdO8X8jQe5o3dLsyOJiDi8wuLSBZ+L7QbDOzVjRGfd9evKKlUAly1bVlM5RCrM3c3C3THhvPjDDmau2sftvUL1DxARkYv419Jd7Mw8QRM/L/5xfQez44jJKnUN4Bm7d+9m0aJFnDpVehF+JVeSEamyWy4LxdfLneSsk6zcfdTsOCIiDm3LARvvLt8DwAs3dKSJpn5dXqUK4NGjR7nmmmto37491157LRkZpZ/IcN999/H444/XSECRc/H38WRUjxYAzFy1z+Q0IiKOq6C4hMdnJ1JiNxjRuRnXdtJdv1LJAjhhwgQ8PT1JTU3F19e37Plbb72VuLi4ag8nciF39w0H4Oedh9h/NNfcMCIiDurtn3eRnHWSgPpe/OP6jmbHEQdRqQL4008/8corr9CiRYtyz7dr1479+/dXazCRi2nTtD4DIppiGDBzVYrZcUREHM6mtOO8d3rq98UbOtHYz8vkROIoKlUAc3Nzy438nXHkyBG8vXU9gdS+cf1aATB73QFOFhSbnEZExHHkF5XwxOxN2A0Y2SWEoR2DzY4kDqRSBfCKK67gk08+KfvaYrFgt9uZPn06V111VbWHE7mYy9sG0LqpHycLipmzLs3sOCIiDuOfP+9i16GTBNT35vmRuutXyqvUMjDTp09nwIABrFu3jsLCQp588km2bdvGsWPHWLlyZU1lFDkvNzcL4/qGM/nbbXwcv58xMeG4uWlJGBFxbRtTs3n/l9Kp35dv7EgjTf3KH1RqBDA6OprNmzfTq1cvBg0aRG5uLjfddBMbN26kTZs2NZVR5IJu6t6CBj4e7DuSy/LkQ2bHEREx1e+nfm/oGsLgDpr6lbNVagQQIDg4mOeff74msohcEj9vD267LJQPftvHjJUpXB0ZZHYkERHTvLkkmT2Hc2nawJspmvqV86h0ATx+/Dhr1qzh0KFD2O32ct8bM2ZMtQUTqYwxMeF8uGIfv+06wu5DJ2gb2MDsSCIitW5DajYf/LoXgJdv7ERDX039yrlVqgB+99133HnnneTm5tKgQYNyH79lsVhUAMU0oY19GRgVxE/bs5ixMoWXbuxkdiQRkVr1+6nfm7o1Z1C0ZkPk/Cp1DeDjjz/OPffcw4kTJzh+/DjZ2dllj2PHjtVURpEKObMkzLwNB7HlFZmcRkSkdr3+UxJ7D+cS2MCb567T1K9cWKUK4MGDB3n44YfPuRagiNn6tG5MZHADThWV8NW6VLPjiIjUmvX7j/HfFaUfiznt5k5YfT1NTiSOrlIFcMiQIaxbt66msohUicViYVy/cAA+XrWf4hL7hV8gIlIHnCos4YnZmzEMuLl7C90IJxVSqWsAhw8fzsSJE9m+fTudOnXC07P8vzBGjhxZreFEKuv6rs2ZtnAnB4+fYsmOLIZ21Ieei0jd9tpPSew7kkuQvzfPXhdtdhxxEhbDMIyKbuzmdv4BQ4vFQklJSbWEchY5OTlYrVZsNhv+/v5mx5HTpi/ayf8t20OvVo35+oEYs+OIiNSYtSnHuOX9eAwDZoy7jKsiAs2O5BR0/q7kFLDdbj/vw9XKnziu2D7huLtZWLPvGNvSbWbHERGpEacKS5g4exOGAaN7tFD5k0qpUAG89tprsdn+dyJ96aWXOH78eNnXR48eJTpaw87iGIKtPgw7/aHnM1emmBtGRKSGvLpoJylH82hm9eGZEToHS+VUqAAuWrSIgoKCsq9feeWVcsu+FBcXk5SUVP3pRC7RmSVhvt2UztGTBRfZWkTEuazee5SZq1IAmHZzZ6z1dNevVE6FCuAfLxOsxGWDIqbo3rIhXVpYKSy288VqLQkjInVHXmExE+eU3vV722WhXNm+qdmRxAlV6hpAEWdRuiRM6Sjgpwn7KdKSMCJSR7wal0TqsTxCrD78fXiU2XHESVWoAFoslnIf+3bmORFHdm2nZjRt4M2hEwX8uCXD7DgiIlWW8Iep3wY+mvqVS1OhdQANw2Ds2LF4e3sDkJ+fz4MPPoifnx9AuesDRRyFl4cbd/UO480lycxYmcL1XZubHUlE5JLlFhQzcc4mAG7v1ZIrNPUrVVChAnj33XeX+/quu+46a5sxY8ZUTyKRanRH75b837LdJKYdZ2NqNt1aNjI7kojIJXklbidpx07RvGE9nr420uw44uQqVABnzJhR0zlEakTTBt6M6NKMeRsOMnNVigqgiDilVXuO8En8fgBe0dSvVAPdBCJ13j2nbwb5YXMGWTn5JqcREamc3IJinpyzGYA7e7ekf7sAkxNJXaACKHVex+ZWLgtvRLHd4LOE/WbHERGplKkLd3Agu3Tqd9K1uutXqofDF8Ds7GxiY2OxWq1YrVZiY2PLfQrJuWRlZTF27FhCQkLw9fVl6NCh7Nq1q9w2BQUFPPTQQwQEBODn58fIkSM5cOBADb4TMdOZJWG+WJ1KfpE+tlBEnMPK3Uf4LKF0LdPpozpT37tCV26JXJTDF8A77riDxMRE4uLiiIuLIzExkdjY2PNubxgGN9xwA3v37uXbb79l48aNhIWFMXDgQHJzc8u2e/TRR5k/fz6zZs1ixYoVnDx5khEjRugzjeuowdFBhFh9OJpbyHeb0s2OIyJyUSd/N/Ub2yeMvm019SvVx2I48Md67Nixg+joaBISEujduzcACQkJxMTEsHPnTiIiIs56TXJyMhEREWzdupUOHToAUFJSQmBgIK+88gr33XcfNpuNpk2b8umnn3LrrbcCkJ6eTmhoKD/++CNDhgypUL6cnBysVis2mw1/f/9qetdSU95bvodX4nYS3cyfHx7ur7UsRcShPT1/C1+sTiW0cT3iHrkCP43+VRudvx18BDA+Ph6r1VpW/gD69OmD1Wpl1apV53zNmTUJfXx8yp5zd3fHy8uLFStWALB+/XqKiooYPHhw2TYhISF07NjxvD/3zM/Oyckp9xDncXuvUHw83diekcOafccu/gIREZP8tutw2cdYvnpzF5U/qXYOXQAzMzMJDAw86/nAwEAyMzPP+ZrIyEjCwsKYNGkS2dnZFBYWMm3aNDIzM8nIyCj7uV5eXjRqVH5JkKCgoPP+XICpU6eWXYtotVoJDQ2twruT2tbQ14sbu7UAYMbKFHPDiIicx4n8Ip46PfV7d0wYMW2amJxI6iJTCuCUKVPKPl7ufI9169YB5/7IOcMwzjt95+npydy5c0lOTqZx48b4+vqyfPlyhg0bhru7+wVzXejnAkyaNAmbzVb2SEtLq8S7Fkcwtm84AD9tz+RAdp65YUREzuHlH3eQbsunZWNfnhqmBZ+lZpgypjx+/Hhuu+22C24THh7O5s2bycrKOut7hw8fJigo6Lyv7dGjB4mJidhsNgoLC2natCm9e/emZ8+eAAQHB1NYWEh2dna5UcBDhw7Rt2/f8/5cb2/vso/DE+cUEdyAfm2bsHL3UT6N368lFUTEofySfJgv15QOLkwf1RlfL039Ss0wZQQwICCAyMjICz58fHyIiYnBZrOxZs2asteuXr0am812waJ2htVqpWnTpuzatYt169Zx/fXXA6UF0dPTk8WLF5dtm5GRwdatWyv0c8W5jetbuiTMl2tSySssNjmNiEipnPwi/ja3dOp3bN9werfW1K/UHIe+BjAqKoqhQ4dy//33k5CQQEJCAvfffz8jRowodwdwZGQk8+fPL/t69uzZLF++vGwpmEGDBnHDDTeU3fRhtVq59957efzxx/n555/ZuHEjd911F506dWLgwIG1/j6ldl0dGUhYE19y8ouZv/Gg2XFERAB48fvtZNjyCWviy5NDz17lQqQ6OXQBBPj888/p1KkTgwcPZvDgwXTu3JlPP/203DZJSUnYbLayrzMyMoiNjSUyMpKHH36Y2NhYvvzyy3KvefPNN7nhhhu45ZZb6NevH76+vnz33XcXvU5QnJ+bm4UxMeEAzFyZggOvhCQiLmJZ0iG+XncAiwWmj+qiqV+pcQ69DqCj0zpCzisnv4iYl38mt7CET+/txeXtmpodSURclO1UEUPe/JXMnHzu6deKZ6+LNjtSnafztxOMAIrUBH8fT0b3LF3GR0vCiIiZXvh+O5k5+bQK8GPiEE39Su1QARSXdffpJWGW7jzEviO5F95YRKQGLN2ZxZz1Z6Z+O1PPS5chSe1QARSX1SrAj6siSqd+P16VYm4YEXE5trwiJs3bAsC9/VrRM7yxyYnElagAiksb1690SZg56w9wIr/I5DQi4kqe/34bWTkFtA7w4wlN/UotUwEUl3Z5uwDaBtbnZEExs9cdMDuOiLiIJduzmLfhIG4WmD66Cz6emvqV2qUCKC7NYrGUfTzcx/Ep2O26KV5EatbxvEImzS+d+r3v8tb0CGt0kVeIVD8VQHF5N3Vvjr+PB/uP5rEs6ZDZcUSkjnv+u+0cPlFAm6Z+PDaovdlxxEWpAIrL8/Xy4LZeLQEtCSMiNeunbZnM31g69fuapn7FRCqAIsCYmDDcLLBi9xGSs06YHUdE6qDs3EKenr8VgD9d0YZuLTX1K+ZRARQBWjTyZXB0MAAztSSMiNSAKd9t48jJAtoF1ufRge3MjiMuTgVQ5LSx/cIBmLfhAMfzCs0NIyJ1StzWTL5NTNddv+IwVABFTuvdqjFRzfzJL7Iza22a2XFEpI44llvIM9+U3vX74JVt6Bra0NxAIqgAipSxWCyMOz0K+MmqFIpL7OYGEpE64bkF2zhyspD2QfV5RFO/4iBUAEV+Z2SXEBr7eZFuy2fx9iyz44iIk1u4JYPvNqXj7mbhtdFd8PbQ1K84BhVAkd/x8XTnDi0JIyLV4OjJAp75pvSu3z9f2YbOLRqaG0jkd1QARf4gNiYMDzcLa1KOsfWgzew4IuKknl2wjaO5hUQENeCha9qaHUekHBVAkT8I8vfh2k7NAI0Cisil+WFzBj9szsDdzcLrt2jqVxyPCqDIOZy5GeS7TekcOVlgbhgRcSpHThYw+dvSqd+/DmhDx+ZWkxOJnE0FUOQcurVsRJfQhhSW2PlidarZcUTESRiGweRvtnIst5DI4AaMv1p3/YpjUgEUOY97To8Cfpqwn8JiLQkjIhf3/eYMFm7NxOP0Xb9eHjrNimPSkSlyHsM6NiOwgTeHTxTw45YMs+OIiIM7fKKAZ89M/V7VVlO/4tBUAEXOw8vDjdg+YQDM0OcDi8gFGIbBM99sITuviKhm/vz1Kt31K45NBVDkAm7v3RIvdzc2pR1nQ2q22XFExEEt2JTOom1ZeLhZeF1Tv+IEdISKXEBAfW9Gdg0BtCSMiJzboRP5PLdgGwAPXd2O6BB/kxOJXJwKoMhFnFkSZuGWDDJt+eaGERGHYhgGf5+/leN5RXQI8ecvV7UxO5JIhagAilxEhxArvVo1pthu8FnCfrPjiIgD+TYxncXbs/B0L13w2dNdp1VxDjpSRSpgXN9wAL5Yk0p+UYm5YUTEIRzK+d/U78NXtyMyWFO/4jxUAEUqYFB0EM0b1uNYbiELEtPNjiMiJjMMg6fnb8F2qohOza08OEBTv+JcVABFKsDD3Y0xMf9bEsYwDJMTiYiZ5m88yJIdh/Byd+O10Zr6FeejI1akgm67rCX1PN3ZkZHD6n3HzI4jIibJyslnyump30cGtiMiuIHJiUQqTwVQpIKsvp7c2L05ADNW7jM5jYiYwTAMJs3bQk5+MZ1bWHngitZmRxK5JCqAIpVw5maQxduzSDuWZ24YEal1czccZOnO0qnf10d3wUNTv+KkdOSKVEK7oAZc3i4AuwGfakkYEZeSacvn+e9Kp34fHdSOdkGa+hXnpQIoUkljT48CzlqTSl5hsblhRKRWGIbB3+Zt5kR+MV1CG/KnyzX1K85NBVCkkq6KCCS8iS85+cXM3XDQ7DgiUgtmrz/A8qTDeHm48frozpr6FaenI1ikktzcLNx9ehRw5sp92O1aEkakLks/fooXvtsOwOOD2tM2UFO/4vxUAEUuwageLajv7cGew7ms2H3E7DgiUkNKp363cKKgmG4tG3Kfpn6ljlABFLkEDXw8GdWjBaAlYUTqsq/WpvFrcunU7/RRXXB3s5gdSaRaqACKXKKxfcOxWGBZ0mH2Hj5pdhwRqWYHj5/ixR92ADBxcARtA+ubnEik+qgAilyi8AA/ro4IBODjVSnmhhGRamUYBn+bu5mTBcX0CGvEPf1bmR1JpFqpAIpUwbh+pSeFOesPkJNfZHIaEakuX65J47ddR/D2cGP6qM6a+pU6RwVQpAr6tW1Cu8D65BaWMHvdAbPjiEg1OJCdx0s/lN71O3FIBK2baupX6h4VQJEqsFgsjO0XDpROA5doSRgRp2YYBk/N3UxuYQk9wxqVjfKL1DUqgCJVdFO3FljreZJ6LI+lOw+ZHUdEquDz1ams3H0UH083po/WXb9Sd6kAilRRPS93busVCsDMVVoSRsRZpR3L4+UfS+/6fXJIJK0C/ExOJFJzVABFqkFsnzDcLLBy91GSMk+YHUdEKsluN3hyzmbyCkvoFd647DO/ReoqFUCRatCikS9DOgQDGgUUcUafr95P/N6j1PN059VRnXHT1K/UcSqAItXkzMXi8zYcJDu30OQ0IlJRqUfzmLpwJwBPDY0gXFO/4gJUAEWqyWXhjegQ4k9BsZ1Za9PMjiMiFWC3G0ycs4m8whJ6t2rMmJhwsyOJ1AoVQJFqYrFYyq4b+jQ+heISu7mBROSiPk3Yz+p9x/D1cmf6qC6a+hWXoQIoUo2u6xJCEz8v0m35LNqWZXYcEbmA/UdzmXZ66vdvwyJp2cTX5EQitUcFUKQa+Xi6c2fvloBuBhFxZKVTv5s5VVRCTOsm3NU7zOxIIrVKBVCkmt3VJwwPNwtrU7J5/5c9+nQQEQf0cXwKa05P/equX3FFKoAi1SzQ34fYmNLRhKkLd3LL+/HsPXzS5FQicsa+I7m8Elc69Tvp2ihCG2vqV1yPCqBIDXh2RDTTbupEfW8P1u/PZtg/f+O/v+3VaKCIyUoXfN5EfpGdfm2bcGevlmZHEjGFCqBIDbBYLNzWqyWLJlzB5e0CKCi28+IPO7j1/Xj2Hck1O56Iy5qxKoW1Kdn4ebnzys2a+hXXpQIoUoOaN6zHJ/f04uUbO+Hn5c66/dkM++evfLRiH3aNBorUqr2HT/Lq6anfp4dH0aKRpn7FdakAitQwi8XCHb1LRwP7tW1CfpGdf3y/ndv+k0CKRgNFakXJ6bt+C4rt9G8bwB2a+hUXpwIoUktaNPLls3t78+INHfHzcmdNyjGG/vNXZqzUaKBITZuxch/r92dT39uDV0Z1xmLR1K+4NhVAkVpksVi4q08YcY9eQd82paOBz3+3nds+SCD1aJ7Z8UTqpD2HTzJ9URIAzwyPonnDeiYnEjGfCqCICUIbl44GvnB9B3y93Fmz7xhD3vqVj1elaDRQpBqV2A2emL2JgmI7V7Rvyq2XhZodScQhqACKmMTNzUJsTDiLHr2CPq0bc6qohOcWbOOO/2o0UKS6/Pe3vWxMPU4Dbw+m3dRJU78ip6kAipgstLEvX9zXh39c34F6nu4k7C29NvDTeI0GilTF7kMneH1xMgCTR0QToqlfkTIOXwCzs7OJjY3FarVitVqJjY3l+PHjF3xNVlYWY8eOJSQkBF9fX4YOHcquXbvKvn/s2DEeeughIiIi8PX1pWXLljz88MPYbLYafjci5+bmZmFMTDhxj15Or1aNySssYfK327jzv6tJO6bRQJHKyi8q4YnZmykstjMgoimje7YwO5KIQ3H4AnjHHXeQmJhIXFwccXFxJCYmEhsbe97tDcPghhtuYO/evXz77bds3LiRsLAwBg4cSG5u6ZIb6enppKen89prr7FlyxZmzpxJXFwc9957b229LZFzCmvix6z7+zDlumjqeboTv/coQ976lU8T9ms0UKQCDMNgwaZ0rnn9FxLTjtPAx4OpmvoVOYvFMAyHPavs2LGD6OhoEhIS6N27NwAJCQnExMSwc+dOIiIiznpNcnIyERERbN26lQ4dOgBQUlJCYGAgr7zyCvfdd985f9fs2bO56667yM3NxcPDo0L5cnJysFqt2Gw2/P39L/Fdipzb/qO5TJy9mTUpxwDo17YJr9zcWYvXipzHxtRsXvh+OxtSjwPQzOrDq6M6c3m7puYGE4ej87eDjwDGx8djtVrLyh9Anz59sFqtrFq16pyvKSgoAMDHx6fsOXd3d7y8vFixYsV5f9eZg+BC5a+goICcnJxyD5GaEtbEj1l/6sOzI6Lx8XRj5e6jDHnzVz5fvR8H/nebSK07ePwUj8zayI3vrmJD6nHqebrz2KD2LH18gMqfyHk4dAHMzMwkMDDwrOcDAwPJzMw852siIyMJCwtj0qRJZGdnU1hYyLRp08jMzCQjI+Ocrzl69CgvvPACDzzwwAXzTJ06texaRKvVSmiolhOQmuXmZuGe/q1Y+MgVXBbeiNzCEv4+fytjPlrDweOnzI4nYqrcgmJe/ymJq19bzreJ6VgsMKpHC5ZPHMDD17Sjnpe72RFFHJYpBXDKlClYLJYLPtatWwdwzus2DMM47/Ucnp6ezJ07l+TkZBo3boyvry/Lly9n2LBhuLuf/ccgJyeH4cOHEx0dzXPPPXfB3JMmTcJms5U90tLSLuHdi1ReqwA/Zv0phskjovH2cOO3XUcY8uavfLkmVaOB4nJK7AZfr01jwGvL+dfS3RQU2+nVqjHfje/Pa6O7EOTvc/EfIuLiKnaxWzUbP348t9122wW3CQ8PZ/PmzWRlZZ31vcOHDxMUFHTe1/bo0YPExERsNhuFhYU0bdqU3r1707Nnz3LbnThxgqFDh1K/fn3mz5+Pp6fnBTN5e3vj7e19wW1Eaoq7m4V7+7fiqoimTJyzmfX7s5k0bwsLt2Yy7aZOWuJCXEL8nqO88P12tmeUXoLTsrEvT18byZAOwbrRQ6QSnOImkNWrV9OrVy8AVq9eTZ8+fc57E8i57Nq1i8jISBYuXMjgwYOB0pG/IUOG4O3tzY8//oivb+UvrNdFpGKWErvBjJX7mL4oiYJiOw28PXhmRBS39AzVSVDqpJQjubz84w5+2l46KNDAx4OHr27HmL5heHtoqlcqR+dvBy+AAMOGDSM9PZ33338fgD/96U+EhYXx3XfflW0TGRnJ1KlTufHGG4HSO3qbNm1Ky5Yt2bJlC4888gg9evRg7ty5QOnI36BBg8jLy2P+/Pn4+fmV/aymTZuec6r4XHQAidn2HD7JE7M3sfH0XY9Xtm/KtJs70cyq0UCpG2ynivjXz7v4OD6FohIDdzcLd/RqyaMD29GkvmZk5NLo/G3SFHBlfP755zz88MNlI3cjR47knXfeKbdNUlJSuUWcMzIyeOyxx8jKyqJZs2aMGTOGyZMnl31//fr1rF69GoC2bduW+1n79u0jPDy8ht6NSPVq07Q+cx7sy4cr9vLaT8n8knyYwW/+yuQR0Yzu0UKjgeK0ikvsfLEmlTcXJ5OdVwSU/gPnmeFRtAtqYHI6Eefn8COAjkz/ghBHsvtQ6WhgYtpxAK6KaMrUmzoTbNUF8eJcliUd4qUfdrD70EkA2gXW5+/DoxgQcfaqECKXQudvFcAq0QEkjqbEbvDBb3t5Y3EyhcV2Gvh48Nx1Hbi5e3ONBorDS8o8wUs/7uDX5MMANPbzYsKg9tx+WSge7g69apk4GZ2/VQCrRAeQOKpdWSd4Ys5mNp0eDbw6MpCpN3XS8hjikI6eLOCNxcl8uSYVuwGe7hbG9WvFX69qi7XehVdnELkUOn+rAFaJDiBxZMUldv7z217eWryLwhI7/qdHA2/SaKA4iILiEmauTOGdpbs5UVAMwNAOwUy6NpKwJn4XebXIpdP5WwWwSnQAiTNIzjrBE7M3sflA6Y1SA6MCefnGTgRqNFBMYhgGcVszmbpwJ6nH8gDo2NyfZ4ZH06d1E5PTiSvQ+VsFsEp0AImzKC6x8/6ve3lrSTJFJQbWep5MGRnNDV01Gii1a8sBGy98v501KccACGzgzcQhEdzcvQVubjoWpXbo/K0CWCU6gMTZJGWWjgZuOXhmNDCIl2/qSGADjQZKzcq05fPqop3M23AQAB9PN/50RRseuKI1ft4OvyKZ1DE6f6sAVokOIHFGRSV2/r18D28v3UVRiUFDX0+eH9mBkV1CNBoo1S6vsJj//LqX93/Zy6miEgBu7NaciUMi9PGFYhqdv1UAq0QHkDizHRk5PDF7E9vSSz9TdXB0EC/d2ImmDfTpClJ1drvBN4kHeTUuicycfAB6hDVi8ohouoY2NDecuDydv1UAq0QHkDi7ohI77y3fw9s/76LYrtFAqR5rU47xwvfby248atGoHn8bFsnwTs10XIlD0PlbBbBKdABJXbE9vXQ0cHtG6Wjg0A7BvHhjRwL0WatSCWnH8pi2cCc/bMkAoL63B3+5qg339GuFj2fFPmNdpDbo/K0CWCU6gKQuKSqx83/LdvPO0t0U2w0a+Xrywg0dGdE5xOxo4uBO5BfxzrLdzFiRQmGJHTcL3HpZSx4b1F6XFIhD0vlbBbBKdABJXbQt3cbjX29iZ+YJAK7tFMw/rtdooJytuMTOV+vSeOOnZI7mFgLQv20Afx8eRVQz/U0Ux6XztwpglegAkrqqsNjOO8t28+6y0tHAxn5evHB9R4Z3bmZ2NHEQv+06zIvf7yApq/QfCq2b+vH3a6O4OjJQ1/mJw9P5WwWwSnQASV239aCNJ2b/bzRweOdm/GNkB5poNNBl7T50kpd/3MHSnYcAsNbz5NGB7birTxie7m4mpxOpGJ2/VQCrRAeQuILCYjvvLN3F/y3fQ4ndoImfFy/e0JFhnTQa6Eqycwt5a0kyn61OpcRu4OFmITYmjEeuaUdDXy+z44lUis7fKoBVogNIXMmWA6WjgWem/EZ0bsY/ru9IYz+d/OuywmI7n8Sn8PbPu8jJLwZKP0Fm0rWRtGla3+R0IpdG528VwCrRASSupqC4hH/9vJv3fikdDQyo78WLN3RiaMdgs6NJNTMMg8Xbs5i6cCf7juQCEBncgMkjounXNsDkdCJVo/O3CmCV6AASV7X5wHEe/3oTuw6dBGBklxCeH9mBRhoNrBO2pdt48fsdxO89CkBAfW+eGNye0T1DcXfTDR7i/HT+VgGsEh1A4soKikv455Jd/PuXPdiN0pLw8o0dGdxBo4HO6tCJfF5flMzX69MwDPDycOO+/q34y1Vtqe/tYXY8kWqj87cKYJXoABKBxLTjPDF7E7tPjwbe0DWEKSM76MYAJ5JfVMKHK/bx7rLd5BaWAKXXeD41NJLQxr4mpxOpfjp/qwBWiQ4gkVL5RSW8tWQX//m1dDSwaQNvXr6xE4Oig8yOJhdgGAYLNqXzalwSB4+fAqBLaEOeHRFFj7DGJqcTqTk6f6sAVokOIJHyNqZm88TsTew5XHrTwE3dmvPcdR2w+nqanEz+aENqNi98v52NqccBCLH68NSwSK7rHIKbrvOTOk7nbxXAKtEBJHK2/KIS3lyczAe/7cVuQGADb6be1IlrojQa6AgOHj/FKwt3smBTOgC+Xu78+co23Hd5a+p5uZucTqR26PytAlglOoBEzm/D6dHAvWdGA7s357kRGg00S25BMe8t38MHv+2loNiOxQKjurfgiSERBPn7mB1PpFbp/K0CWCU6gEQuLL+ohNd/SuK/K/ZhGBDk7820mzpzVWSg2dFcRondYO76A0z/KYnDJwoA6N2qMZNHRNOxudXkdCLm0PlbBbBKdACJVMz6/ceYOHsze08vKDyqRwsmj4jGWk+jgTVp1Z4jvPj9DrZn5AAQ1sSXp6+NYnB0EBaLrvMT16XztwpglegAEqm4/KISXluUxIcrS0cDg/19mHpzJ66K0Ghgddt3JJeXf9zB4u1ZADTw8eCRa9oxJiYcLw83k9OJmE/nbxXAKtEBJFJ5a1OOMXH2JlKO5gFwS88WPDMiGn8fjQZWlS2viLeX7uKT+BSKSgzc3Szc2bsljw5sr89sFvkdnb9VAKtEB5DIpTlVWML0RUnMWFU6GtjM6sO0mztzZfumZkdzSkUldr5YncpbS5LJzisCYEBEU/5+bRTtghqYnE7E8ej8rQJYJTqARKpmzb5jTJyzif2nRwNvuyyUvw+PooFGAyvEMAyWJx3mxR+2l6292C6wPs+MiFaZFrkAnb9VAKtEB5BI1eUVFvNqXBIzV6UApQsSvzKqM5e3U4G5kKTME7z4w3Z+23UEgMZ+Xjw2qD23XRaKh7uu8xO5EJ2/VQCrRAeQSPVZvfcoE+dsJvVY6Wjg7b1a8vS1kRoN/IMjJwt4Y3Eys9akYjfAy92Ncf3C+evVbXUdpUgF6fytAlglOoBEqtcfRwObN6zHKzd3pn+7AHODOYCC4hJmrEzh/5bu5kRBMQDDOgbzt2GRhDXxMzmdiHPR+VsFsEp0AInUjPg9R3ly7ibSjp0C4I7eLXn62ijqe3uYnKz2GYbBwq2ZTF24o2x/dGpu5ZnhUfRu3cTkdCLOSedvFcAq0QEkUnNyC4p5JW4nn8TvB0pHA6eP6kzftq4zGrj5wHFe/H4Ha1KOAaWfpDJxSCQ3dWuOm5sWcha5VDp/qwBWiQ4gkZq3as8RnpyzmQPZpaNfd/VpyaRhUfjV4dHADNsppi9KYt6GgwD4eLrxpyva8OCVrfH1qrvvW6S26PytAlglOoBEasfJgmKmLdzBZwmpALRoVI9XR3Wmb5u6NRqYV1jM+7/s5f1f95BfZAfgpm7NmTg0gmbWeianE6k7dP5WAawSHUAitWvl7tLRwIPHS0cDx8SE8dTQSKcfDbTbDeZvPMj0RUlk5uQD0DOsEZNHRNMltKG54UTqIJ2/VQCrRAeQSO07WVDMyz/u4IvVpaOBoY3rMX1UF/o46Q0Ra/Yd48UftrP5gA0oHd2cNCyKazsFY7HoOj+RmqDztwpglegAEjHPb7sO89SczaTbSkfM7o4J46lhkU5zjVzq0Tymxe3gxy2ZANT39uCvV7VlXL9wfDzdTU4nUrfp/K0CWCU6gETMdSK/iJd/3MGXa9IAaNnYl+mjOjv08ig5+UX837LdzFiRQmGJHTcL3NarJY8Nak9AfW+z44m4BJ2/VQCrRAeQiGP4Nfkwf5tbOhposcDdMeE8OTTCoUYDi0vszFqbxpuLkzmaWwjA5e0C+PvwKCKD9fdDpDbp/K0CWCU6gEQcR05+ES99v4Ov1pWOBoY18WX6qC70atXY5GSlBfXFH7aTnHUSgNZN/XhmeBRXRQTqOj8RE+j8rQJYJTqARBzP8qRDTJq3hYzTo4Hj+rZi4pAI6nnV/nV1uw+d4KUfdrAs6TAADX09efSadtzZJwxPd7dazyMipXT+VgGsEh1AIo4pJ7+IF7/fztfrDgDQKsCP6aM60zO8dkYDs3MLeWtJMp+tTqXEbuDhZmFMTDiPXNMOq69nrWQQkfPT+VsFsEp0AIk4tmU7D/G3eZvJyinAYoF7+7XiiSERNXaXbWGxnU/iU3j7513k5BcDMCg6iEnDImndtH6N/E4RqTydv1UAq0QHkIjjs50q4oXvtzNnfeloYOsAP6aP7kKPsEbV9jsMw+Cn7VlM/XEHKUfzAIhq5s/k4VEu9dnFIs5C528VwCrRASTiPJbuzOJvc7dw6EQBbha47/LWPDaofZVHA7el23jx+x3E7z0KQEB9byYOac+oHqG4u+kGDxFHpPO3CmCV6AAScS62vCKe/34b8zYcBErvxn1tdBe6t6z8aOChnHxe+ymJ2esPYBjg5eHG/Ze34s8D2lLfyT+aTqSu0/lbBbBKdACJOKcl27N4ev7/RgPvv6I1EwZWbDQwv6iE//62l3eX7yGvsASA67qE8NTQCFo08q3p6CJSDXT+VgGsEh1AIs7reF4hz3+3nfkbS0cD2wbW57XRXega2vCc2xuGwYJN6bwal8TB46cA6BrakMkjoqv1ekIRqXk6f6sAVokOIBHnt/j0aODh06OBf7qiDY8ObFduNHD9/mxe/GE7G1OPAxBi9eGpYZGM7BKihZxFnJDO3yqAVaIDSKRuOJ5XyJQF2/gmMR2AdqdHA5vU9+KVuCS+21T6vK+XO38Z0Ib7Lm9dY0vJiEjN0/lbBbBKdACJ1C2LtmXy9/lbOHKyEHc3C+5uFgqL7VgsMLpHC54YHEGgv4/ZMUWkinT+Bt2qJiJy2pAOwfQKb8xzC7axYFM6JXaDPq0b88zwaDo2t5odT0Sk2qgAioj8TiM/L96+vRu3XhaK3TDo3zZA1/mJSJ2jAigicg799AkeIlKHuZkdQERERERqlwqgiIiIiItRARQRERFxMSqAIiIiIi5GBVBERETExagAioiIiLgYhy+A2dnZxMbGYrVasVqtxMbGcvz48Qu+Jisri7FjxxISEoKvry9Dhw5l165d59zWMAyGDRuGxWLhm2++qf43ICIiIuJgHL4A3nHHHSQmJhIXF0dcXByJiYnExsaed3vDMLjhhhvYu3cv3377LRs3biQsLIyBAweSm5t71vZvvfWWFnkVERERl+LQC0Hv2LGDuLg4EhIS6N27NwAffPABMTExJCUlERERcdZrdu3aRUJCAlu3bqVDhw4AvPvuuwQGBvLll19y3333lW27adMm3njjDdauXUuzZs1q502JiIiImMyhRwDj4+OxWq1l5Q+gT58+WK1WVq1adc7XFBQUAODj878PbHd3d8fLy4sVK1aUPZeXl8ftt9/OO++8Q3BwcIXyFBQUkJOTU+4hIiIi4mwcugBmZmYSGBh41vOBgYFkZmae8zWRkZGEhYUxadIksrOzKSwsZNq0aWRmZpKRkVG23YQJE+jbty/XX399hfNMnTq17FpEq9VKaGho5d+UiIiIiMlMKYBTpkzBYrFc8LFu3TqAc16fZxjGea/b8/T0ZO7cuSQnJ9O4cWN8fX1Zvnw5w4YNw93dHYAFCxawdOlS3nrrrUrlnjRpEjabreyRlpZWuTcuIiIi4gBMuQZw/Pjx3HbbbRfcJjw8nM2bN5OVlXXW9w4fPkxQUNB5X9ujRw8SExOx2WwUFhbStGlTevfuTc+ePQFYunQpe/bsoWHDhuVed/PNN3P55ZezfPnyc/5cb29vvL29L/zmRERERBycxTAMw+wQ57Njxw6io6NZvXo1vXr1AmD16tX06dOHnTt3nvMmkHPZtWsXkZGRLFy4kMGDB5OZmcmRI0fKbdOpUyf++c9/ct1119GqVasK/VybzUbDhg1JS0vD39+/cm9ORERETJGTk0NoaCjHjx/HarWaHccchoMbOnSo0blzZyM+Pt6Ij483OnXqZIwYMaLcNhEREca8efPKvv7666+NZcuWGXv27DG++eYbIywszLjpppsu+HsAY/78+ZXKlpaWZgB66KGHHnrooYcTPtLS0ip13q9LHHoZGIDPP/+chx9+mMGDBwMwcuRI3nnnnXLbJCUlYbPZyr7OyMjgscceIysri2bNmjFmzBgmT55c7dlCQkJIS0ujQYMGDrGW4Jl/0WhEspT2x9m0T8rT/ihP+6M87Y/y6tL+MAyDEydOEBISYnYU0zj0FLBUTk5ODlarFZvN5vT/cVYH7Y+zaZ+Up/1RnvZHedof5Wl/1C0OvQyMiIiIiFQ/FUARERERF6MCWId4e3vz3HPPaama07Q/zqZ9Up72R3naH+Vpf5Sn/VG36BpAERERERejEUARERERF6MCKCIiIuJiVABFREREXIwKoIiIiIiLUQF0QgcPHuSuu+6iSZMm+Pr60rVrV9avX1/2fcMwmDJlCiEhIdSrV48BAwawbds2ExPXvAvtk6KiIp566ik6deqEn58fISEhjBkzhvT0dJNT15yLHSO/98ADD2CxWHjrrbdqN2Qtqsj+2LFjByNHjsRqtdKgQQP69OlDamqqSYlr1sX2x8mTJxk/fjwtWrSgXr16REVF8d5775mYuOaEh4djsVjOevz1r38FXO/v6YX2hyv+La3LVACdTHZ2Nv369cPT05OFCxeyfft2Xn/9dRo2bFi2zauvvsobb7zBO++8w9q1awkODmbQoEGcOHHCvOA16GL7JC8vjw0bNjB58mQ2bNjAvHnzSE5OZuTIkeYGryEVOUbO+Oabb1i9enWd/jikiuyPPXv20L9/fyIjI1m+fDmbNm1i8uTJ+Pj4mBe8hlRkf0yYMIG4uDg+++wzduzYwYQJE3jooYf49ttvzQteQ9auXUtGRkbZY/HixQCMHj0acL2/pxfaH672t7TOM/FziOUSPPXUU0b//v3P+3273W4EBwcb06ZNK3suPz/fsFqtxr///e/aiFjrLrZPzmXNmjUGYOzfv7+GUpmnovvjwIEDRvPmzY2tW7caYWFhxptvvlnz4UxQkf1x6623GnfddVctJTJXRfZHhw4djH/84x/lnuvevbvxzDPP1GQ0h/DII48Ybdq0Mex2u0v+Pf2j3++Pc6nLf0vrOo0AOpkFCxbQs2dPRo8eTWBgIN26deODDz4o+/6+ffvIzMxk8ODBZc95e3tz5ZVXsmrVKjMi17iL7ZNzsdlsWCyWc46KObuK7A+73U5sbCwTJ06kQ4cOJiWtHRfbH3a7nR9++IH27dszZMgQAgMD6d27N9988415oWtQRY6P/v37s2DBAg4ePIhhGCxbtozk5GSGDBliUuraUVhYyGeffcY999yDxWJxyb+nv/fH/XEudflvaZ1ndgOVyvH29ja8vb2NSZMmGRs2bDD+/e9/Gz4+PsbHH39sGIZhrFy50gCMgwcPlnvd/fffbwwePNiMyDXuYvvkj06dOmX06NHDuPPOO2s5ae2oyP54+eWXjUGDBpX9q74ujwBebH9kZGQYgOHr62u88cYbxsaNG42pU6caFovFWL58ucnpq19Fjo+CggJjzJgxBmB4eHgYXl5exieffGJi6trx1VdfGe7u7mV/P13x7+nv/XF//FFd/1ta16kAOhlPT08jJiam3HMPPfSQ0adPH8Mw/vcHKz09vdw29913nzFkyJBay1mbLrZPfq+wsNC4/vrrjW7duhk2m622Itaqi+2PdevWGUFBQeX+qNflAnix/XHw4EEDMG6//fZy21x33XXGbbfdVms5a0tF/nuZPn260b59e2PBggXGpk2bjH/9619G/fr1jcWLF9d23Fo1ePBgY8SIEWVfu+Lf09/74/74PVf4W1rXaQrYyTRr1ozo6Ohyz0VFRZXdrRgcHAxAZmZmuW0OHTpEUFBQ7YSsZRfbJ2cUFRVxyy23sG/fPhYvXoy/v39txqw1F9sfv/32G4cOHaJly5Z4eHjg4eHB/v37efzxxwkPDzchcc262P4ICAjAw8OjQsdQXXCx/XHq1Cmefvpp3njjDa677jo6d+7M+PHjufXWW3nttdfMiFwr9u/fz5IlS7jvvvvKnnPFv6dnnGt/nOEqf0vrOhVAJ9OvXz+SkpLKPZecnExYWBgArVq1Ijg4uOzOLSi9juOXX36hb9++tZq1tlxsn8D//mDt2rWLJUuW0KRJk9qOWWsutj9iY2PZvHkziYmJZY+QkBAmTpzIokWLzIhcoy62P7y8vLjssssuegzVFRfbH0VFRRQVFeHmVv704O7ujt1ur7WctW3GjBkEBgYyfPjwsudc8e/pGefaH+Baf0vrPLOHIKVy1qxZY3h4eBgvvfSSsWvXLuPzzz83fH19jc8++6xsm2nTphlWq9WYN2+esWXLFuP22283mjVrZuTk5JiYvOZcbJ8UFRUZI0eONFq0aGEkJiYaGRkZZY+CggKT01e/ihwjf1SXp4Arsj/mzZtneHp6Gv/5z3+MXbt2Gf/6178Md3d347fffjMxec2oyP648sorjQ4dOhjLli0z9u7da8yYMcPw8fEx3n33XROT15ySkhKjZcuWxlNPPXXW91zt76lhnH9/uNrf0rpOBdAJfffdd0bHjh0Nb29vIzIy0vjPf/5T7vt2u9147rnnjODgYMPb29u44oorjC1btpiUtnZcaJ/s27fPAM75WLZsmXmha9DFjpE/qssF0DAqtj8+/PBDo23btoaPj4/RpUsX45tvvjEhae242P7IyMgwxo4da4SEhBg+Pj5GRESE8frrr593KRBnt2jRIgMwkpKSzvqeK/49Pd/+cMW/pXWZxTAMw4yRRxERERExh64BFBEREXExKoAiIiIiLkYFUERERMTFqACKiIiIuBgVQBEREREXowIoIiIi4mJUAEVERERcjAqgiLik8PBw3nrrLbNjiIiYQgVQRJzOddddx8CBA8/5vfj4eCwWCxs2bKjlVCIizkMFUESczr333svSpUvZv3//Wd/76KOP6Nq1K927dzchmYiIc1ABFBGnM2LECAIDA5k5c2a55/Py8vjqq6+49957mTt3Lh06dMDb25vw8HBef/318/68lJQULBYLiYmJZc8dP34ci8XC8uXLAVi+fDkWi4VFixbRrVs36tWrx9VXX82hQ4dYuHAhUVFR+Pv7c/vtt5OXl1f2cwzD4NVXX6V169bUq1ePLl26MGfOnOrcHSIilaYCKCJOx8PDgzFjxjBz5kx+/3Hms2fPprCwkJiYGG655RZuu+02tmzZwpQpU5g8efJZhfFSTJkyhXfeeYdVq1aRlpbGLbfcwltvvcUXX3zBDz/8wOLFi/nXv/5Vtv0zzzzDjBkzeO+999i2bRsTJkzgrrvu4pdffqlyFhGRS2Uxfv/XU0TESezcuZOoqCiWLl3KVVddBcCVV15J8+bNsVgsHD58mJ9++qls+yeffJIffviBbdu2AaU3gTz66KM8+uijpKSk0KpVKzZu3EjXrl2B0hHARo0asWzZMgYMGMDy5cu56qqrWLJkCddccw0A06ZNY9KkSezZs4fWrVsD8OCDD5KSkkJcXBy5ubkEBASwdOlSYmJiyrLcd9995OXl8cUXX9TGrhIROYtGAEXEKUVGRtK3b18++ugjAPbs2cNvv/3GPffcw44dO+jXr1+57fv168euXbsoKSmp0u/t3Llz2f8PCgrC19e3rPydee7QoUMAbN++nfz8fAYNGkT9+vXLHp988gl79uypUg4RkarwMDuAiMiluvfeexk/fjz/93//x4wZMwgLC+Oaa67BMAwsFku5bS802eHm5nbWNkVFRefc1tPTs+z/WyyWcl+fec5utwOU/e8PP/xA8+bNy23n7e19sbcnIlJjNAIoIk7rlltuwd3dnS+++IKPP/6YcePGYbFYiI6OZsWKFeW2XbVqFe3bt8fd3f2sn9O0aVMAMjIyyp77/Q0hlyo6Ohpvb29SU1Np27ZtuUdoaGiVf76IyKXSCKCIOK369etz66238vTTT2Oz2Rg7diwAjz/+OJdddhkvvPACt956K/Hx8bzzzju8++675/w59erVo0+fPkybNo3w8HCOHDnCM888U+V8DRo04IknnmDChAnY7Xb69+9PTk4Oq1aton79+tx9991V/h0iIpdCI4Ai4tTuvfdesrOzGThwIC1btgSge/fufP3118yaNYuOHTvy7LPP8o9//KOsIJ7LRx99RFFRET179uSRRx7hxRdfrJZ8L7zwAs8++yxTp04lKiqKIUOG8N1339GqVatq+fkiIpdCdwGLiIiIuBiNAIqIiIi4GBVAERERERejAigiIiLiYlQARURERFyMCqCIiIiIi1EBFBEREXExKoAiIiIiLkYFUERERMTFqACKiIiIuBgVQBEREREXowIoIiIi4mJUAEVERERczP8DlapuQsP0WcUAAAAASUVORK5CYII=", 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -639,12 +341,7 @@ } ], "source": [ - "from IPython.display import Image, display\n", - "\n", - "plot_task = [t for t in wg.tasks if t.name.startswith('plot_energy_volume_curve')][0]\n", - "plot_file = f\"{plot_task.node.get_remote_workdir()}/evcurve.png\"\n", - "\n", - "display(Image(filename=str(plot_file)))\n" + "wg.run()" ] }, { @@ -657,28 +354,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "b8e3c2ca-2672-4e9d-aada-63344842dbcf", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jan/mambaforge/lib/python3.12/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n", - " \"cipher\": algorithms.TripleDES,\n", - "/home/jan/mambaforge/lib/python3.12/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n", - " \"class\": algorithms.TripleDES,\n" - ] - } - ], + "outputs": [], "source": [ "from python_workflow_definition.jobflow import load_workflow_json" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "54a24ff6c569094e", "metadata": {}, "outputs": [], @@ -688,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "48a27146-7372-40ab-8b02-e2a9283d4748", "metadata": {}, "outputs": [], @@ -698,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "98fa5694-2cc6-44e9-a6e7-2cc71b4f48ce", "metadata": {}, "outputs": [ @@ -706,39 +392,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-22 17:52:08,581 INFO Started executing jobs locally\n", - "2025-03-22 17:52:08,776 INFO Starting job - get_bulk_structure (b9442893-363a-41f1-bd3e-2c2f78ef15c6)\n", - "2025-03-22 17:52:08,782 INFO Finished job - get_bulk_structure (b9442893-363a-41f1-bd3e-2c2f78ef15c6)\n", - "2025-03-22 17:52:08,783 INFO Starting job - get_dict (918c0c42-b666-4be7-a054-0988ced41e6d)\n", - "2025-03-22 17:52:08,786 INFO Finished job - get_dict (918c0c42-b666-4be7-a054-0988ced41e6d)\n", - "2025-03-22 17:52:08,786 INFO Starting job - calculate_qe (1ed31005-a7b0-49d2-b8c2-1b94b0ec2f1f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-22 17:52:34,281 INFO Finished job - calculate_qe (1ed31005-a7b0-49d2-b8c2-1b94b0ec2f1f)\n", - "2025-03-22 17:52:34,281 INFO Starting job - generate_structures (c9f14676-8efa-4f5f-b586-6017a11a4a6c)\n", - "2025-03-22 17:52:34,288 INFO Finished job - generate_structures (c9f14676-8efa-4f5f-b586-6017a11a4a6c)\n", - "2025-03-22 17:52:34,289 INFO Starting job - get_dict (0a20a1b7-b152-4de6-bd89-75e85b1b0376)\n", - "2025-03-22 17:52:34,291 INFO Finished job - get_dict (0a20a1b7-b152-4de6-bd89-75e85b1b0376)\n", - "2025-03-22 17:52:34,292 INFO Starting job - get_dict (f21744e3-7be8-4a38-9dc1-bad7ccd96680)\n", - "2025-03-22 17:52:34,294 INFO Finished job - get_dict (f21744e3-7be8-4a38-9dc1-bad7ccd96680)\n", - "2025-03-22 17:52:34,295 INFO Starting job - get_dict (98b6aa5b-eb82-48f1-b20c-9e466bcb4ea9)\n", - "2025-03-22 17:52:34,296 INFO Finished job - get_dict (98b6aa5b-eb82-48f1-b20c-9e466bcb4ea9)\n", - "2025-03-22 17:52:34,297 INFO Starting job - get_dict (078fe358-13fa-4e26-9ee4-7def6dedef56)\n", - "2025-03-22 17:52:34,299 INFO Finished job - get_dict (078fe358-13fa-4e26-9ee4-7def6dedef56)\n", - "2025-03-22 17:52:34,301 INFO Starting job - get_dict (9d23a203-f275-44ec-8ffb-7264aa2b69cf)\n", - "2025-03-22 17:52:34,303 INFO Finished job - get_dict (9d23a203-f275-44ec-8ffb-7264aa2b69cf)\n", - "2025-03-22 17:52:34,303 INFO Starting job - calculate_qe (d7cb4651-5ff6-4690-8f43-8d8e8d6fcacf)\n" + "2025-04-01 10:57:31,434 INFO Started executing jobs locally\n", + "2025-04-01 10:57:31,534 INFO Starting job - get_bulk_structure (c4dbc1c3-08e2-4517-88b1-55b2b14fae2b)\n", + "2025-04-01 10:57:31,539 INFO Finished job - get_bulk_structure (c4dbc1c3-08e2-4517-88b1-55b2b14fae2b)\n", + "2025-04-01 10:57:31,541 INFO Starting job - get_dict (a6e8e886-8bbb-488a-8125-46cdfb4dedb4)\n", + "2025-04-01 10:57:31,545 INFO Finished job - get_dict (a6e8e886-8bbb-488a-8125-46cdfb4dedb4)\n", + "2025-04-01 10:57:31,545 INFO Starting job - calculate_qe (d0d3f8f7-9c1e-4e26-a49a-9637c759bd64)\n" ] }, { @@ -752,8 +411,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-22 17:52:39,498 INFO Finished job - calculate_qe (d7cb4651-5ff6-4690-8f43-8d8e8d6fcacf)\n", - "2025-03-22 17:52:39,499 INFO Starting job - calculate_qe (38988913-354d-4b93-ad21-21a90f4f4149)\n" + "2025-04-01 10:57:49,723 INFO Finished job - calculate_qe (d0d3f8f7-9c1e-4e26-a49a-9637c759bd64)\n", + "2025-04-01 10:57:49,724 INFO Starting job - generate_structures (3c77f030-1b28-45dc-934e-d85147676f34)\n", + "2025-04-01 10:57:49,737 INFO Finished job - generate_structures (3c77f030-1b28-45dc-934e-d85147676f34)\n", + "2025-04-01 10:57:49,738 INFO Starting job - get_dict (5baee8fe-01f3-4a49-b589-8eb2547bd61f)\n", + "2025-04-01 10:57:49,741 INFO Finished job - get_dict (5baee8fe-01f3-4a49-b589-8eb2547bd61f)\n", + "2025-04-01 10:57:49,742 INFO Starting job - get_dict (1c0e5563-998a-4adb-8036-3c3f65dc3e4a)\n", + "2025-04-01 10:57:49,744 INFO Finished job - get_dict (1c0e5563-998a-4adb-8036-3c3f65dc3e4a)\n", + "2025-04-01 10:57:49,745 INFO Starting job - get_dict (e5660385-b8ff-4a58-9349-520757d80a7d)\n", + "2025-04-01 10:57:49,748 INFO Finished job - get_dict (e5660385-b8ff-4a58-9349-520757d80a7d)\n", + "2025-04-01 10:57:49,748 INFO Starting job - get_dict (07bf6634-c9b2-4d98-b6db-96147a5e89f7)\n", + "2025-04-01 10:57:49,751 INFO Finished job - get_dict (07bf6634-c9b2-4d98-b6db-96147a5e89f7)\n", + "2025-04-01 10:57:49,751 INFO Starting job - get_dict (dd376f88-1ac0-41e1-8e75-db0cc598a45e)\n", + "2025-04-01 10:57:49,754 INFO Finished job - get_dict (dd376f88-1ac0-41e1-8e75-db0cc598a45e)\n", + "2025-04-01 10:57:49,755 INFO Starting job - calculate_qe (39512b85-df17-48f3-a064-9f322b68a2ac)\n" ] }, { @@ -767,14 +438,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-22 17:52:45,103 INFO Finished job - calculate_qe (38988913-354d-4b93-ad21-21a90f4f4149)\n", - "2025-03-22 17:52:45,104 INFO Starting job - calculate_qe (f053b6d4-2efb-445d-8355-33fc6f8d4a5d)\n" + "2025-04-01 10:57:53,583 INFO Finished job - calculate_qe (39512b85-df17-48f3-a064-9f322b68a2ac)\n", + "2025-04-01 10:57:53,584 INFO Starting job - calculate_qe (227561a6-47b5-4ba4-bd3f-2e59c6089e0b)\n", + "2025-04-01 10:57:57,547 INFO Finished job - calculate_qe (227561a6-47b5-4ba4-bd3f-2e59c6089e0b)\n", + "2025-04-01 10:57:57,548 INFO Starting job - calculate_qe (ff2920f8-72e0-4b58-8828-d1b30f60fcb1)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] }, @@ -782,8 +456,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-22 17:52:51,529 INFO Finished job - calculate_qe (f053b6d4-2efb-445d-8355-33fc6f8d4a5d)\n", - "2025-03-22 17:52:51,530 INFO Starting job - calculate_qe (f6139f33-9dbe-4cee-89ba-d1f8191d2472)\n" + "2025-04-01 10:58:02,366 INFO Finished job - calculate_qe (ff2920f8-72e0-4b58-8828-d1b30f60fcb1)\n", + "2025-04-01 10:58:02,367 INFO Starting job - calculate_qe (bbcd53dc-eb58-4567-b727-476d1b9393d3)\n" ] }, { @@ -797,8 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", 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", "text/plain": [ "
" ] @@ -867,7 +541,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "aiida", "language": "python", "name": "python3" }, @@ -881,7 +555,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.5" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/universal_workflow_qe.ipynb b/universal_workflow_qe.ipynb index dd9512c..b0fde02 100644 --- a/universal_workflow_qe.ipynb +++ b/universal_workflow_qe.ipynb @@ -16,16 +16,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Profile" + "Profile" ] }, - "execution_count": 12, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -38,30 +38,30 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "wg = load_workflow_json(file_name='aiida_qe.json')" + "wg = load_workflow_json(file_name='workflow_qe.json')" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a22b1fa6af494c348ca8f4ebf0255751", + "model_id": "0878f4037df646f280e7a410c0119254", "version_major": 2, "version_minor": 1 }, "text/plain": [ - "NodeGraphWidget(settings={'minimap': True}, style={'width': '90%', 'height': '600px'}, value={'name': 'WorkGra…" + "NodeGraphWidget(positions={'get_list17': [30, 30]}, settings={'minimap': True}, states={'get_dict10': 'FINISHE…" ] }, - "execution_count": 14, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -72,66 +72,66 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "04/01/2025 06:19:04 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_bulk_structure1\n", - "04/01/2025 06:19:05 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_bulk_structure1, type: PyFunction, finished.\n", - "04/01/2025 06:19:05 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict2\n", - "04/01/2025 06:19:07 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict2, type: PyFunction, finished.\n", - "04/01/2025 06:19:07 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe3\n", - "[thinkpad:240834] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", - "04/01/2025 06:19:56 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['energy', 'volume'])\n", - "04/01/2025 06:19:57 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe3, type: PyFunction, finished.\n", - "04/01/2025 06:19:57 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: generate_structures4\n", - "04/01/2025 06:19:59 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: generate_structures4, type: PyFunction, finished.\n", - "04/01/2025 06:19:59 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict7,get_dict9,get_dict11,get_dict13,get_dict15\n", - "04/01/2025 06:20:00 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict7, type: PyFunction, finished.\n", - "04/01/2025 06:20:00 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe8,get_dict9,get_dict11,get_dict13,get_dict15\n", - "[thinkpad:241673] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", - "04/01/2025 06:20:08 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "04/01/2025 06:20:09 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe8, type: PyFunction, finished.\n", - "04/01/2025 06:20:09 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict9,get_dict11,get_dict13,get_dict15\n", - "04/01/2025 06:20:10 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict9, type: PyFunction, finished.\n", - "04/01/2025 06:20:10 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe10,get_dict11,get_dict13,get_dict15\n", - "[thinkpad:241817] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", - "04/01/2025 06:20:18 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "04/01/2025 06:20:18 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe10, type: PyFunction, finished.\n", - "04/01/2025 06:20:18 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict11,get_dict13,get_dict15\n", - "04/01/2025 06:20:20 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict11, type: PyFunction, finished.\n", - "04/01/2025 06:20:20 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe12,get_dict13,get_dict15\n", - "[thinkpad:242008] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", - "04/01/2025 06:20:32 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "04/01/2025 06:20:32 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe12, type: PyFunction, finished.\n", - "04/01/2025 06:20:32 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict13,get_dict15\n", - "04/01/2025 06:20:34 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict13, type: PyFunction, finished.\n", - "04/01/2025 06:20:34 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe14,get_dict15\n", - "[thinkpad:242195] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", - "04/01/2025 06:20:48 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "04/01/2025 06:20:49 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe14, type: PyFunction, finished.\n", - "04/01/2025 06:20:49 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict15\n", - "04/01/2025 06:20:51 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_dict15, type: PyFunction, finished.\n", - "04/01/2025 06:20:51 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe16\n", - "[thinkpad:242533] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n", - "04/01/2025 06:21:04 AM <102368> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", - "04/01/2025 06:21:05 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: calculate_qe16, type: PyFunction, finished.\n", - "04/01/2025 06:21:05 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list5,get_list6\n", - "04/01/2025 06:21:07 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_list5, type: PyFunction, finished.\n", - "04/01/2025 06:21:07 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list6\n", - "04/01/2025 06:21:08 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: get_list6, type: PyFunction, finished.\n", - "04/01/2025 06:21:08 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: plot_energy_volume_curve17\n", - "04/01/2025 06:21:09 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|update_task_state]: Task: plot_energy_volume_curve17, type: PyFunction, finished.\n", - "04/01/2025 06:21:10 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", - "04/01/2025 06:21:10 AM <102368> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [65731|WorkGraphEngine|finalize]: Finalize workgraph.\n" + "04/01/2025 10:47:35 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_bulk_structure1\n", + "04/01/2025 10:47:36 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: get_bulk_structure1, type: PyFunction, finished.\n", + "04/01/2025 10:47:36 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict10\n", + "04/01/2025 10:47:38 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: get_dict10, type: PyFunction, finished.\n", + "04/01/2025 10:47:38 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe2\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:47:57 AM <3350393> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['energy', 'volume'])\n", + "04/01/2025 10:47:58 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: calculate_qe2, type: PyFunction, finished.\n", + "04/01/2025 10:47:58 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: generate_structures3\n", + "04/01/2025 10:47:59 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: generate_structures3, type: PyFunction, finished.\n", + "04/01/2025 10:48:00 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict11,get_dict12,get_dict13,get_dict14,get_dict15\n", + "04/01/2025 10:48:01 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: get_dict11, type: PyFunction, finished.\n", + "04/01/2025 10:48:02 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict12,get_dict13,get_dict14,get_dict15,calculate_qe4\n", + "04/01/2025 10:48:03 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: get_dict12, type: PyFunction, finished.\n", + "04/01/2025 10:48:03 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict13,get_dict14,get_dict15,calculate_qe4,calculate_qe5\n", + "04/01/2025 10:48:05 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: get_dict13, type: PyFunction, finished.\n", + "04/01/2025 10:48:05 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict14,get_dict15,calculate_qe4,calculate_qe5,calculate_qe6\n", + "04/01/2025 10:48:06 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: get_dict14, type: PyFunction, finished.\n", + "04/01/2025 10:48:07 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_dict15,calculate_qe4,calculate_qe5,calculate_qe6,calculate_qe7\n", + "04/01/2025 10:48:08 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: get_dict15, type: PyFunction, finished.\n", + "04/01/2025 10:48:08 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe4,calculate_qe5,calculate_qe6,calculate_qe7,calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:48:13 AM <3350393> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:48:13 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: calculate_qe4, type: PyFunction, finished.\n", + "04/01/2025 10:48:14 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe5,calculate_qe6,calculate_qe7,calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:48:18 AM <3350393> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:48:18 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: calculate_qe5, type: PyFunction, finished.\n", + "04/01/2025 10:48:19 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe6,calculate_qe7,calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:48:24 AM <3350393> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:48:24 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: calculate_qe6, type: PyFunction, finished.\n", + "04/01/2025 10:48:25 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe7,calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:48:30 AM <3350393> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:48:30 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: calculate_qe7, type: PyFunction, finished.\n", + "04/01/2025 10:48:31 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: calculate_qe8\n", + "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", + "04/01/2025 10:48:36 AM <3350393> aiida.orm.nodes.process.calculation.calcfunction.CalcFunctionNode: [WARNING] Found extra results that are not included in the output: dict_keys(['structure'])\n", + "04/01/2025 10:48:37 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: calculate_qe8, type: PyFunction, finished.\n", + "04/01/2025 10:48:37 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list16,get_list17\n", + "04/01/2025 10:48:38 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: get_list16, type: PyFunction, finished.\n", + "04/01/2025 10:48:39 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: get_list17\n", + "04/01/2025 10:48:40 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: get_list17, type: PyFunction, finished.\n", + "04/01/2025 10:48:40 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: plot_energy_volume_curve9\n", + "04/01/2025 10:48:41 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|update_task_state]: Task: plot_energy_volume_curve9, type: PyFunction, finished.\n", + "04/01/2025 10:48:42 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n", + "04/01/2025 10:48:43 AM <3350393> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [51096|WorkGraphEngine|finalize]: Finalize workgraph.\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -144,31 +144,6 @@ "wg.run()\n" ] }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "plot_task = [t for t in wg.tasks if t.name.startswith('plot_energy_volume_curve')][0]\n", - "plot_file = f\"{plot_task.node.get_remote_workdir()}/evcurve.png\"\n", - "\n", - "display(Image(filename=str(plot_file)))\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -178,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -187,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -196,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -219,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -235,27 +210,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jan/mambaforge/lib/python3.12/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n", - " \"cipher\": algorithms.TripleDES,\n", - "/home/jan/mambaforge/lib/python3.12/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n", - " \"class\": algorithms.TripleDES,\n" - ] - } - ], + "outputs": [], "source": [ "from jobflow.managers.local import run_locally" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -273,19 +237,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2025-03-24 12:32:15,747 INFO Started executing jobs locally\n", - "2025-03-24 12:32:16,046 INFO Starting job - get_bulk_structure (041f2e15-6338-4f68-91fb-122e458b20e8)\n", - "2025-03-24 12:32:16,053 INFO Finished job - get_bulk_structure (041f2e15-6338-4f68-91fb-122e458b20e8)\n", - "2025-03-24 12:32:16,054 INFO Starting job - get_dict (d25b2abe-3024-4121-af08-dea958eaee16)\n", - "2025-03-24 12:32:16,056 INFO Finished job - get_dict (d25b2abe-3024-4121-af08-dea958eaee16)\n", - "2025-03-24 12:32:16,057 INFO Starting job - calculate_qe (a78a5ae9-7635-4abf-ad2a-9b5377793e35)\n" + "2025-04-01 10:43:00,064 INFO Started executing jobs locally\n", + "2025-04-01 10:43:00,178 INFO Starting job - get_bulk_structure (fec37bcc-80c3-4dd6-a3fd-edec25d0b82e)\n", + "2025-04-01 10:43:00,181 INFO Finished job - get_bulk_structure (fec37bcc-80c3-4dd6-a3fd-edec25d0b82e)\n", + "2025-04-01 10:43:00,182 INFO Starting job - get_dict (d81a1fa8-1abd-435a-a866-ea84fc3b19b0)\n", + "2025-04-01 10:43:00,184 INFO Finished job - get_dict (d81a1fa8-1abd-435a-a866-ea84fc3b19b0)\n", + "2025-04-01 10:43:00,185 INFO Starting job - calculate_qe (ae08905c-3313-4ba0-bc13-7d2ee7f17f76)\n" ] }, { @@ -299,20 +263,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-24 12:32:41,924 INFO Finished job - calculate_qe (a78a5ae9-7635-4abf-ad2a-9b5377793e35)\n", - "2025-03-24 12:32:41,925 INFO Starting job - generate_structures (5669ec8d-9076-45cb-bb5c-ab6e207ee0ef)\n", - "2025-03-24 12:32:41,934 INFO Finished job - generate_structures (5669ec8d-9076-45cb-bb5c-ab6e207ee0ef)\n", - "2025-03-24 12:32:41,935 INFO Starting job - get_dict 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", 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ID: 9\n", - "The job calculate_qe_e3c0bf43f7edf24d215901bf93271e87 was saved and received the ID: 10\n" + "The job get_bulk_structure_f1e730ed97e30e5439e855d2ac41396f was saved and received the ID: 4\n", + "The job get_dict_bbed7e528d369f5fa02591748be3166d was saved and received the ID: 5\n", + "The job calculate_qe_a8254d13768b11f13ee9368124819d22 was saved and received the ID: 6\n" ] }, { @@ -1248,9 +1212,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job generate_structures_4f19b30f71f1958da8dbd3a89753da5d was saved and received the ID: 11\n", - "The job get_dict_13e432fdcf23d1fa277df2f0a8fdd0eb was saved and received the ID: 12\n", - "The job calculate_qe_936c506dc0da610ea26659ab1a1ff5e7 was saved and received the ID: 13\n" + "The job generate_structures_3f6d1168b6bf78842f871ac2ac64785e was saved and received the ID: 7\n", + "The job get_dict_78147dcf9cf97bce89b29b9156be481e was saved and received the ID: 8\n", + "The job calculate_qe_6a905eb237355b37736372ba371d1cd7 was saved and received the ID: 9\n" ] }, { @@ -1264,8 +1228,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_eab85cbca6391480353d104f46660ea0 was saved and received the ID: 14\n", - "The job calculate_qe_e8256936e58b4db30b0eb588c966b5fb was saved and received the ID: 15\n" + "The job get_dict_03156dccf3d708cfc4ea579c84410584 was saved and received the ID: 10\n", + "The job calculate_qe_0d18bdb479313067edc04c18ad5e9350 was saved and received the ID: 11\n" ] }, { @@ -1279,8 +1243,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_b75a4287000474f9abe3bb080edb15c6 was saved and received the ID: 16\n", - "The job calculate_qe_c1ba1aad76f28e1763f1f3aad86c3ac2 was saved and received the ID: 17\n" + "The job get_dict_cb8acebdccdbf43c1274bc0f369e20d2 was saved and received the ID: 12\n", + "The job calculate_qe_fd424c0e14c41f389a8a2f16aeed1727 was saved and received the ID: 13\n" ] }, { @@ -1294,8 +1258,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_f676b23e7feba42ed75061c1a43ee669 was saved and received the ID: 18\n", - "The job calculate_qe_58ae4770d2bd3904dee9a5a268621254 was saved and received the ID: 19\n" + "The job get_dict_ffb00753416ab53a8c9996d34f96d94f was saved and received the ID: 14\n", + "The job calculate_qe_7d7624485601eea5e9b9025e8a169f3f was saved and received the ID: 15\n" ] }, { @@ -1309,8 +1273,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_dict_a4ae30e61c1e61b3dd6b6f1a94cdb1e9 was saved and received the ID: 20\n", - "The job calculate_qe_e45155c3ed670f48efe449544b1d3cf5 was saved and received the ID: 21\n" + "The job get_dict_182ad0e25ad107a0be68d34a5da97797 was saved and received the ID: 16\n", + "The job calculate_qe_f58ce5cbc0e9ad20d31940dc6b3dab89 was saved and received the ID: 17\n" ] }, { @@ -1324,14 +1288,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "The job get_list_be666759994036119e95398e7f2b8bce was saved and received the ID: 22\n", - "The job get_list_c06fe3108c8f15278a0857a288d83e09 was saved and received the ID: 23\n", - "The job plot_energy_volume_curve_18ffc37f2b5436578a3e562f03613e43 was saved and received the ID: 24\n" + "The job get_list_f3b45e6d3522e7481032cd4c04ea8038 was saved and received the ID: 18\n", + "The job get_list_5b2a5697807693999f4f7fd11cbc24e1 was saved and received the ID: 19\n", + "The job plot_energy_volume_curve_c833d5a782334c26202e03241a80020b was saved and received the ID: 20\n" ] }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -1346,7 +1310,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1390,17 +1354,17 @@ " \n", "
\n", " 0\n", - " 8\n", + " 4\n", " finished\n", " None\n", " get_bulk_structure_f1e730ed97e30e5439e855d2ac41396f\n", " /get_bulk_structure_f1e730ed97e30e5439e855d2ac41396f\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:12.350506\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:43:41.139368\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1408,17 +1372,17 @@ "
\n", "
\n", " 1\n", - " 9\n", + " 5\n", " finished\n", " None\n", - " get_dict_1e47509b88d63a21fd421686554c8f4a\n", - " /get_dict_1e47509b88d63a21fd421686554c8f4a\n", + " get_dict_bbed7e528d369f5fa02591748be3166d\n", + " /get_dict_bbed7e528d369f5fa02591748be3166d\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:12.520232\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:43:41.518318\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1426,17 +1390,17 @@ "
\n", "
\n", " 2\n", - " 10\n", + " 6\n", " finished\n", " None\n", - " calculate_qe_e3c0bf43f7edf24d215901bf93271e87\n", - " /calculate_qe_e3c0bf43f7edf24d215901bf93271e87\n", + " calculate_qe_a8254d13768b11f13ee9368124819d22\n", + " /calculate_qe_a8254d13768b11f13ee9368124819d22\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:12.715777\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:43:41.889294\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1444,17 +1408,17 @@ "
\n", "
\n", " 3\n", - " 11\n", + " 7\n", " finished\n", " None\n", - " generate_structures_4f19b30f71f1958da8dbd3a89753da5d\n", - " /generate_structures_4f19b30f71f1958da8dbd3a89753da5d\n", + " generate_structures_3f6d1168b6bf78842f871ac2ac64785e\n", + " /generate_structures_3f6d1168b6bf78842f871ac2ac64785e\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:39.244095\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:00.853623\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1462,17 +1426,17 @@ "
\n", "
\n", " 4\n", - " 12\n", + " 8\n", " finished\n", " None\n", - " get_dict_13e432fdcf23d1fa277df2f0a8fdd0eb\n", - " /get_dict_13e432fdcf23d1fa277df2f0a8fdd0eb\n", + " get_dict_78147dcf9cf97bce89b29b9156be481e\n", + " /get_dict_78147dcf9cf97bce89b29b9156be481e\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:39.464450\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:01.221846\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1480,17 +1444,17 @@ "
\n", "
\n", " 5\n", - " 13\n", + " 9\n", " finished\n", " None\n", - " calculate_qe_936c506dc0da610ea26659ab1a1ff5e7\n", - " /calculate_qe_936c506dc0da610ea26659ab1a1ff5e7\n", + " calculate_qe_6a905eb237355b37736372ba371d1cd7\n", + " /calculate_qe_6a905eb237355b37736372ba371d1cd7\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:39.604777\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:01.618681\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1498,17 +1462,17 @@ "
\n", "
\n", " 6\n", - " 14\n", + " 10\n", " finished\n", " None\n", - " get_dict_eab85cbca6391480353d104f46660ea0\n", - " /get_dict_eab85cbca6391480353d104f46660ea0\n", + " get_dict_03156dccf3d708cfc4ea579c84410584\n", + " /get_dict_03156dccf3d708cfc4ea579c84410584\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:46.045978\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:05.671596\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1516,17 +1480,17 @@ "
\n", "
\n", " 7\n", - " 15\n", + " 11\n", " finished\n", " None\n", - " calculate_qe_e8256936e58b4db30b0eb588c966b5fb\n", - " /calculate_qe_e8256936e58b4db30b0eb588c966b5fb\n", + " calculate_qe_0d18bdb479313067edc04c18ad5e9350\n", + " /calculate_qe_0d18bdb479313067edc04c18ad5e9350\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:46.427849\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:06.074751\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1534,17 +1498,17 @@ "
\n", "
\n", " 8\n", - " 16\n", + " 12\n", " finished\n", " None\n", - " get_dict_b75a4287000474f9abe3bb080edb15c6\n", - " /get_dict_b75a4287000474f9abe3bb080edb15c6\n", + " get_dict_cb8acebdccdbf43c1274bc0f369e20d2\n", + " /get_dict_cb8acebdccdbf43c1274bc0f369e20d2\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:52.460128\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:10.316256\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1552,17 +1516,17 @@ "
\n", "
\n", " 9\n", - " 17\n", + " 13\n", " finished\n", " None\n", - " calculate_qe_c1ba1aad76f28e1763f1f3aad86c3ac2\n", - " /calculate_qe_c1ba1aad76f28e1763f1f3aad86c3ac2\n", + " calculate_qe_fd424c0e14c41f389a8a2f16aeed1727\n", + " /calculate_qe_fd424c0e14c41f389a8a2f16aeed1727\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:52.598890\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:10.699333\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1570,17 +1534,17 @@ "
\n", "
\n", " 10\n", - " 18\n", + " 14\n", " finished\n", " None\n", - " get_dict_f676b23e7feba42ed75061c1a43ee669\n", - " /get_dict_f676b23e7feba42ed75061c1a43ee669\n", + " get_dict_ffb00753416ab53a8c9996d34f96d94f\n", + " /get_dict_ffb00753416ab53a8c9996d34f96d94f\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:58.613473\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:15.056345\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1588,17 +1552,17 @@ "
\n", "
\n", " 11\n", - " 19\n", + " 15\n", " finished\n", " None\n", - " calculate_qe_58ae4770d2bd3904dee9a5a268621254\n", - " /calculate_qe_58ae4770d2bd3904dee9a5a268621254\n", + " calculate_qe_7d7624485601eea5e9b9025e8a169f3f\n", + " /calculate_qe_7d7624485601eea5e9b9025e8a169f3f\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:33:58.758885\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:15.452264\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1606,17 +1570,17 @@ "
\n", "
\n", " 12\n", - " 20\n", + " 16\n", " finished\n", " None\n", - " get_dict_a4ae30e61c1e61b3dd6b6f1a94cdb1e9\n", - " /get_dict_a4ae30e61c1e61b3dd6b6f1a94cdb1e9\n", + " get_dict_182ad0e25ad107a0be68d34a5da97797\n", + " /get_dict_182ad0e25ad107a0be68d34a5da97797\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:34:05.587973\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:20.443877\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1624,17 +1588,17 @@ "
\n", "
\n", " 13\n", - " 21\n", + " 17\n", " finished\n", " None\n", - " calculate_qe_e45155c3ed670f48efe449544b1d3cf5\n", - " /calculate_qe_e45155c3ed670f48efe449544b1d3cf5\n", + " calculate_qe_f58ce5cbc0e9ad20d31940dc6b3dab89\n", + " /calculate_qe_f58ce5cbc0e9ad20d31940dc6b3dab89\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:34:05.729420\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:20.872942\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1642,17 +1606,17 @@ "
\n", "
\n", " 14\n", - " 22\n", + " 18\n", " finished\n", " None\n", - " get_list_be666759994036119e95398e7f2b8bce\n", - " /get_list_be666759994036119e95398e7f2b8bce\n", + " get_list_f3b45e6d3522e7481032cd4c04ea8038\n", + " /get_list_f3b45e6d3522e7481032cd4c04ea8038\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:34:12.578239\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:26.032279\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1660,17 +1624,17 @@ "
\n", "
\n", " 15\n", - " 23\n", + " 19\n", " finished\n", " None\n", - " get_list_c06fe3108c8f15278a0857a288d83e09\n", - " /get_list_c06fe3108c8f15278a0857a288d83e09\n", + " get_list_5b2a5697807693999f4f7fd11cbc24e1\n", + " /get_list_5b2a5697807693999f4f7fd11cbc24e1\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:34:12.984644\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:26.615405\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1678,17 +1642,17 @@ "
\n", "
\n", " 16\n", - " 24\n", + " 20\n", " finished\n", " None\n", - " plot_energy_volume_curve_18ffc37f2b5436578a3e562f03613e43\n", - " /plot_energy_volume_curve_18ffc37f2b5436578a3e562f03613e43\n", + " plot_energy_volume_curve_c833d5a782334c26202e03241a80020b\n", + " /plot_energy_volume_curve_c833d5a782334c26202e03241a80020b\n", " None\n", - " /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/\n", - " 2025-03-24 12:34:13.125200\n", + " /home/wang_x3/repos/superstar54/python-workflow-definition/test/\n", + " 2025-04-01 10:44:27.049045\n", " None\n", " None\n", - " pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1\n", + " pyiron@mpc3088#1\n", " PythonFunctionContainerJob\n", " 0.4\n", " None\n", @@ -1700,118 +1664,99 @@ ], "text/plain": [ " id status chemicalformula \\\n", - "0 8 finished None \n", - "1 9 finished None \n", - "2 10 finished None \n", - "3 11 finished None \n", - "4 12 finished None \n", - "5 13 finished None \n", - "6 14 finished None \n", - "7 15 finished None \n", - "8 16 finished None \n", - "9 17 finished None \n", - "10 18 finished None \n", - "11 19 finished None \n", - "12 20 finished None \n", - "13 21 finished None \n", - "14 22 finished None \n", - "15 23 finished None \n", - "16 24 finished None \n", + "0 4 finished None \n", + "1 5 finished None \n", + "2 6 finished None \n", + "3 7 finished None \n", + "4 8 finished None \n", + "5 9 finished None \n", + "6 10 finished None \n", + "7 11 finished None \n", + "8 12 finished None \n", + "9 13 finished None \n", + "10 14 finished None \n", + "11 15 finished None \n", + "12 16 finished None \n", + "13 17 finished None \n", + "14 18 finished None \n", + "15 19 finished None \n", + "16 20 finished None \n", "\n", " job \\\n", "0 get_bulk_structure_f1e730ed97e30e5439e855d2ac41396f \n", - "1 get_dict_1e47509b88d63a21fd421686554c8f4a \n", - "2 calculate_qe_e3c0bf43f7edf24d215901bf93271e87 \n", - "3 generate_structures_4f19b30f71f1958da8dbd3a89753da5d \n", - "4 get_dict_13e432fdcf23d1fa277df2f0a8fdd0eb \n", - "5 calculate_qe_936c506dc0da610ea26659ab1a1ff5e7 \n", - "6 get_dict_eab85cbca6391480353d104f46660ea0 \n", - "7 calculate_qe_e8256936e58b4db30b0eb588c966b5fb \n", - "8 get_dict_b75a4287000474f9abe3bb080edb15c6 \n", - "9 calculate_qe_c1ba1aad76f28e1763f1f3aad86c3ac2 \n", - "10 get_dict_f676b23e7feba42ed75061c1a43ee669 \n", - "11 calculate_qe_58ae4770d2bd3904dee9a5a268621254 \n", - "12 get_dict_a4ae30e61c1e61b3dd6b6f1a94cdb1e9 \n", - "13 calculate_qe_e45155c3ed670f48efe449544b1d3cf5 \n", - "14 get_list_be666759994036119e95398e7f2b8bce \n", - "15 get_list_c06fe3108c8f15278a0857a288d83e09 \n", - "16 plot_energy_volume_curve_18ffc37f2b5436578a3e562f03613e43 \n", + "1 get_dict_bbed7e528d369f5fa02591748be3166d \n", + "2 calculate_qe_a8254d13768b11f13ee9368124819d22 \n", + "3 generate_structures_3f6d1168b6bf78842f871ac2ac64785e \n", + "4 get_dict_78147dcf9cf97bce89b29b9156be481e \n", + "5 calculate_qe_6a905eb237355b37736372ba371d1cd7 \n", + "6 get_dict_03156dccf3d708cfc4ea579c84410584 \n", + "7 calculate_qe_0d18bdb479313067edc04c18ad5e9350 \n", + "8 get_dict_cb8acebdccdbf43c1274bc0f369e20d2 \n", + "9 calculate_qe_fd424c0e14c41f389a8a2f16aeed1727 \n", + "10 get_dict_ffb00753416ab53a8c9996d34f96d94f \n", + "11 calculate_qe_7d7624485601eea5e9b9025e8a169f3f \n", + "12 get_dict_182ad0e25ad107a0be68d34a5da97797 \n", + "13 calculate_qe_f58ce5cbc0e9ad20d31940dc6b3dab89 \n", + "14 get_list_f3b45e6d3522e7481032cd4c04ea8038 \n", + "15 get_list_5b2a5697807693999f4f7fd11cbc24e1 \n", + "16 plot_energy_volume_curve_c833d5a782334c26202e03241a80020b \n", "\n", " subjob projectpath \\\n", "0 /get_bulk_structure_f1e730ed97e30e5439e855d2ac41396f None \n", - "1 /get_dict_1e47509b88d63a21fd421686554c8f4a None \n", - "2 /calculate_qe_e3c0bf43f7edf24d215901bf93271e87 None \n", - "3 /generate_structures_4f19b30f71f1958da8dbd3a89753da5d None \n", - "4 /get_dict_13e432fdcf23d1fa277df2f0a8fdd0eb None \n", - "5 /calculate_qe_936c506dc0da610ea26659ab1a1ff5e7 None \n", - "6 /get_dict_eab85cbca6391480353d104f46660ea0 None \n", - "7 /calculate_qe_e8256936e58b4db30b0eb588c966b5fb None \n", - "8 /get_dict_b75a4287000474f9abe3bb080edb15c6 None \n", - "9 /calculate_qe_c1ba1aad76f28e1763f1f3aad86c3ac2 None \n", - "10 /get_dict_f676b23e7feba42ed75061c1a43ee669 None \n", - "11 /calculate_qe_58ae4770d2bd3904dee9a5a268621254 None \n", - "12 /get_dict_a4ae30e61c1e61b3dd6b6f1a94cdb1e9 None \n", - "13 /calculate_qe_e45155c3ed670f48efe449544b1d3cf5 None \n", - "14 /get_list_be666759994036119e95398e7f2b8bce None \n", - "15 /get_list_c06fe3108c8f15278a0857a288d83e09 None \n", - "16 /plot_energy_volume_curve_18ffc37f2b5436578a3e562f03613e43 None \n", - "\n", - " project \\\n", - "0 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "1 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "2 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "3 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "4 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "5 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "6 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "7 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "8 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "9 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "10 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "11 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "12 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "13 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "14 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "15 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", - "16 /home/jan/notebooks/2025/2025-03-22-compare-workflows/test/ \n", + "1 /get_dict_bbed7e528d369f5fa02591748be3166d None \n", + "2 /calculate_qe_a8254d13768b11f13ee9368124819d22 None \n", + "3 /generate_structures_3f6d1168b6bf78842f871ac2ac64785e None \n", + "4 /get_dict_78147dcf9cf97bce89b29b9156be481e None \n", + "5 /calculate_qe_6a905eb237355b37736372ba371d1cd7 None \n", + "6 /get_dict_03156dccf3d708cfc4ea579c84410584 None \n", + "7 /calculate_qe_0d18bdb479313067edc04c18ad5e9350 None \n", + "8 /get_dict_cb8acebdccdbf43c1274bc0f369e20d2 None \n", + "9 /calculate_qe_fd424c0e14c41f389a8a2f16aeed1727 None \n", + "10 /get_dict_ffb00753416ab53a8c9996d34f96d94f None \n", + "11 /calculate_qe_7d7624485601eea5e9b9025e8a169f3f None \n", + "12 /get_dict_182ad0e25ad107a0be68d34a5da97797 None \n", + "13 /calculate_qe_f58ce5cbc0e9ad20d31940dc6b3dab89 None \n", + "14 /get_list_f3b45e6d3522e7481032cd4c04ea8038 None \n", + "15 /get_list_5b2a5697807693999f4f7fd11cbc24e1 None \n", + "16 /plot_energy_volume_curve_c833d5a782334c26202e03241a80020b None \n", "\n", - " timestart timestop totalcputime \\\n", - "0 2025-03-24 12:33:12.350506 None None \n", - "1 2025-03-24 12:33:12.520232 None None \n", - "2 2025-03-24 12:33:12.715777 None None \n", - "3 2025-03-24 12:33:39.244095 None None \n", - "4 2025-03-24 12:33:39.464450 None None \n", - "5 2025-03-24 12:33:39.604777 None None \n", - "6 2025-03-24 12:33:46.045978 None None \n", - "7 2025-03-24 12:33:46.427849 None None \n", - "8 2025-03-24 12:33:52.460128 None None \n", - "9 2025-03-24 12:33:52.598890 None None \n", - "10 2025-03-24 12:33:58.613473 None None \n", - "11 2025-03-24 12:33:58.758885 None None \n", - "12 2025-03-24 12:34:05.587973 None None \n", - "13 2025-03-24 12:34:05.729420 None None \n", - "14 2025-03-24 12:34:12.578239 None None \n", - "15 2025-03-24 12:34:12.984644 None None \n", - "16 2025-03-24 12:34:13.125200 None None \n", + " project \\\n", + "0 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "1 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "2 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "3 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "4 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "5 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "6 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "7 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "8 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "9 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "10 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "11 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "12 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "13 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "14 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "15 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", + "16 /home/wang_x3/repos/superstar54/python-workflow-definition/test/ \n", "\n", - " computer \\\n", - "0 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "1 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "2 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "3 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "4 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "5 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "6 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "7 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "8 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "9 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "10 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "11 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "12 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "13 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "14 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "15 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", - "16 pyiron@p200300e77f488c66bae2561c878c14a2.dip0.t-ipconnect.de#1 \n", + " timestart timestop totalcputime computer \\\n", + "0 2025-04-01 10:43:41.139368 None None pyiron@mpc3088#1 \n", + "1 2025-04-01 10:43:41.518318 None None pyiron@mpc3088#1 \n", + "2 2025-04-01 10:43:41.889294 None None pyiron@mpc3088#1 \n", + "3 2025-04-01 10:44:00.853623 None None pyiron@mpc3088#1 \n", + "4 2025-04-01 10:44:01.221846 None None pyiron@mpc3088#1 \n", + "5 2025-04-01 10:44:01.618681 None None pyiron@mpc3088#1 \n", + "6 2025-04-01 10:44:05.671596 None None pyiron@mpc3088#1 \n", + "7 2025-04-01 10:44:06.074751 None None pyiron@mpc3088#1 \n", + "8 2025-04-01 10:44:10.316256 None None pyiron@mpc3088#1 \n", + "9 2025-04-01 10:44:10.699333 None None pyiron@mpc3088#1 \n", + "10 2025-04-01 10:44:15.056345 None None pyiron@mpc3088#1 \n", + "11 2025-04-01 10:44:15.452264 None None pyiron@mpc3088#1 \n", + "12 2025-04-01 10:44:20.443877 None None pyiron@mpc3088#1 \n", + "13 2025-04-01 10:44:20.872942 None None pyiron@mpc3088#1 \n", + "14 2025-04-01 10:44:26.032279 None None pyiron@mpc3088#1 \n", + "15 2025-04-01 10:44:26.615405 None None pyiron@mpc3088#1 \n", + "16 2025-04-01 10:44:27.049045 None None pyiron@mpc3088#1 \n", "\n", " hamilton hamversion parentid masterid \n", "0 PythonFunctionContainerJob 0.4 None None \n", @@ -1833,7 +1778,7 @@ "16 PythonFunctionContainerJob 0.4 None None " ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1851,7 +1796,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1860,30 +1805,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n", "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ From 7c4d9df0b9b08685d8fab956f008cea98783bd09 Mon Sep 17 00:00:00 2001 From: superstar54 Date: Tue, 1 Apr 2025 11:01:10 +0200 Subject: [PATCH 4/7] Bump aiida-workgraph to 0.5.1 --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 641ff13..830006c 100644 --- a/environment.yml +++ b/environment.yml @@ -11,4 +11,4 @@ dependencies: - jobflow=0.1.19 - pygraphviz=1.14 - optimade=1.2.3 -- aiida-workgraph=0.5.0 +- aiida-workgraph=0.5.1 From 6a4552e86e738ac2c2b7da7ffe47bdf009ced8d4 Mon Sep 17 00:00:00 2001 From: Julian Geiger Date: Wed, 2 Apr 2025 09:57:56 +0200 Subject: [PATCH 5/7] Install AiiDA WG from pip in environment.yml --- environment.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 830006c..0d09eb2 100644 --- a/environment.yml +++ b/environment.yml @@ -11,4 +11,6 @@ dependencies: - jobflow=0.1.19 - pygraphviz=1.14 - optimade=1.2.3 -- aiida-workgraph=0.5.1 +- pip +- pip: + - aiida-workgraph=0.5.1 From 0cf1c9c6995d78bac94a9e476ce2336854135298 Mon Sep 17 00:00:00 2001 From: Julian Geiger Date: Wed, 2 Apr 2025 09:58:22 +0200 Subject: [PATCH 6/7] fix environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 0d09eb2..1a072fc 100644 --- a/environment.yml +++ b/environment.yml @@ -13,4 +13,4 @@ dependencies: - optimade=1.2.3 - pip - pip: - - aiida-workgraph=0.5.1 + - aiida-workgraph==0.5.1 From 4c695b29246d2e8367a4a8b50a8db17db92ff1eb Mon Sep 17 00:00:00 2001 From: Julian Geiger Date: Wed, 2 Apr 2025 14:23:40 +0200 Subject: [PATCH 7/7] Also change aiida_simple workflow to use new setup. --- aiida_simple.ipynb | 504 ++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 503 insertions(+), 1 deletion(-) diff --git a/aiida_simple.ipynb b/aiida_simple.ipynb index 82ddf2b..33b5bdf 100644 --- a/aiida_simple.ipynb +++ b/aiida_simple.ipynb @@ -1 +1,503 @@ -{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.8"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Simple Workflow with aiida","metadata":{}},{"cell_type":"markdown","source":"## Define workflow with aiida","metadata":{}},{"cell_type":"code","source":"from python_workflow_definition.aiida import write_workflow_json\n\nfrom aiida_workgraph import WorkGraph, task\nfrom aiida import load_profile\nload_profile()\n\nworkflow_json_filename = \"aiida_to_jobflow_simple.json\"","metadata":{"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"from simple_workflow import (\n add_x_and_y as _add_x_and_y, \n add_x_and_y_and_z as _add_x_and_y_and_z,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"@task.pythonjob()\ndef pickle_node(value):\n \"\"\"Handle data nodes\"\"\"\n return value","metadata":{"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"code","source":"add_x_and_y = task.pythonjob(outputs=[\"x\", \"y\", \"z\"])(_add_x_and_y)\nadd_x_and_y_and_z = task.pythonjob()(_add_x_and_y_and_z)","metadata":{"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"# TODO: Create inputs rather than tasks out of data nodes\nwg = WorkGraph(\"wg-simple\")","metadata":{"trusted":true},"outputs":[],"execution_count":5},{"cell_type":"code","source":"helper_task1 = wg.add_task(pickle_node, name=\"x\", value=1)\nhelper_task2 = wg.add_task(pickle_node, name=\"y\", value=2)","metadata":{"trusted":true},"outputs":[],"execution_count":6},{"cell_type":"code","source":"add_x_and_y_task = wg.add_task(\n add_x_and_y,\n name=\"add_x_and_y\",\n x=helper_task1.outputs.result,\n y=helper_task2.outputs.result,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":7},{"cell_type":"code","source":"add_x_and_y_and_z_task = wg.add_task(\n add_x_and_y_and_z,\n name=\"add_x_and_y_and_z\",\n x=add_x_and_y_task.outputs.x,\n y=add_x_and_y_task.outputs.y,\n z=add_x_and_y_task.outputs.z,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":8},{"cell_type":"code","source":"write_workflow_json(wg=wg, file_name=workflow_json_filename)","metadata":{"trusted":true},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"{'nodes': {'0': 1,\n '1': 2,\n '2': 'simple_workflow.add_x_and_y',\n '3': 'simple_workflow.add_x_and_y_and_z'},\n 'edges': [{'target': 2,\n 'targetHandle': 'x',\n 'source': 0,\n 'sourceHandle': None},\n {'target': 2, 'targetHandle': 'y', 'source': 1, 'sourceHandle': None},\n {'target': 3, 'targetHandle': 'x', 'source': 2, 'sourceHandle': 'x'},\n {'target': 3, 'targetHandle': 'y', 'source': 2, 'sourceHandle': 'y'},\n {'target': 3, 'targetHandle': 'z', 'source': 2, 'sourceHandle': 'z'}]}"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"!cat {workflow_json_filename}","metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"{\n \"nodes\": {\n \"0\": 1,\n \"1\": 2,\n \"2\": \"simple_workflow.add_x_and_y\",\n \"3\": \"simple_workflow.add_x_and_y_and_z\"\n },\n \"edges\": [\n {\n \"target\": 2,\n \"targetHandle\": \"x\",\n \"source\": 0,\n \"sourceHandle\": null\n },\n {\n \"target\": 2,\n \"targetHandle\": \"y\",\n \"source\": 1,\n \"sourceHandle\": null\n },\n {\n \"target\": 3,\n \"targetHandle\": \"x\",\n \"source\": 2,\n \"sourceHandle\": \"x\"\n },\n {\n \"target\": 3,\n \"targetHandle\": \"y\",\n \"source\": 2,\n \"sourceHandle\": \"y\"\n },\n {\n \"target\": 3,\n \"targetHandle\": \"z\",\n \"source\": 2,\n \"sourceHandle\": \"z\"\n }\n ]\n}"}],"execution_count":10},{"cell_type":"markdown","source":"## Load Workflow with jobflow","metadata":{}},{"cell_type":"code","source":"from python_workflow_definition.jobflow import load_workflow_json","metadata":{"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":"/srv/conda/envs/notebook/lib/python3.12/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n \"cipher\": algorithms.TripleDES,\n/srv/conda/envs/notebook/lib/python3.12/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n \"class\": algorithms.TripleDES,\n"}],"execution_count":11},{"cell_type":"code","source":"from jobflow.managers.local import run_locally","metadata":{"trusted":true},"outputs":[],"execution_count":12},{"cell_type":"code","source":"flow = load_workflow_json(file_name=workflow_json_filename)","metadata":{"trusted":true},"outputs":[],"execution_count":13},{"cell_type":"code","source":"result = run_locally(flow)\nresult","metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"2025-03-22 09:38:21,738 INFO Started executing jobs locally\n2025-03-22 09:38:22,164 INFO Starting job - add_x_and_y (2a742a0c-14eb-4969-aac0-8d63a4ffe064)\n2025-03-22 09:38:22,166 INFO Finished job - add_x_and_y (2a742a0c-14eb-4969-aac0-8d63a4ffe064)\n2025-03-22 09:38:22,167 INFO Starting job - add_x_and_y_and_z (bd204fad-4ce1-4e7e-954e-11f6bfd19ff4)\n2025-03-22 09:38:22,171 INFO Finished job - add_x_and_y_and_z (bd204fad-4ce1-4e7e-954e-11f6bfd19ff4)\n2025-03-22 09:38:22,172 INFO Finished executing jobs locally\n"},{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"{'2a742a0c-14eb-4969-aac0-8d63a4ffe064': {1: Response(output={'x': 1, 'y': 2, 'z': 3}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/jovyan'))},\n 'bd204fad-4ce1-4e7e-954e-11f6bfd19ff4': {1: Response(output=6, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/jovyan'))}}"},"metadata":{}}],"execution_count":14},{"cell_type":"markdown","source":"## Load Workflow with pyiron_base","metadata":{}},{"cell_type":"code","source":"from pyiron_base import Project","metadata":{"trusted":true},"outputs":[],"execution_count":15},{"cell_type":"code","source":"from python_workflow_definition.pyiron_base import load_workflow_json","metadata":{"trusted":true},"outputs":[],"execution_count":16},{"cell_type":"code","source":"pr = Project(\"test\")\npr.remove_jobs(recursive=True, silently=True)","metadata":{"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"25edafe2cf6944ec9608ac3d0328867f"}},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"delayed_object = load_workflow_json(project=pr, file_name=workflow_json_filename)\ndelayed_object.draw()","metadata":{"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"","image/svg+xml":"\n\n\n\n\ncreate_function_job_c4ec5c7a7dd53d01300058ba6c796595\n\ncreate_function_job=<pyiron_base.project.delayed.DelayedObject object at 0x7f18837cb620>\n\n\n\nx_29b9ebbd9ab08db91ecdc7d0038e5fbc\n\nx=<pyiron_base.project.delayed.DelayedObject object at 0x7f18837cb290>\n\n\n\nx_29b9ebbd9ab08db91ecdc7d0038e5fbc->create_function_job_c4ec5c7a7dd53d01300058ba6c796595\n\n\n\n\n\nx_1d847da32ecaabf6731c38f798c3d4ce\n\nx=1\n\n\n\nx_1d847da32ecaabf6731c38f798c3d4ce->x_29b9ebbd9ab08db91ecdc7d0038e5fbc\n\n\n\n\n\ny_bee009126d70c89ce914fe7ac7c3f63a\n\ny=<pyiron_base.project.delayed.DelayedObject object at 0x7f18837cb260>\n\n\n\nx_1d847da32ecaabf6731c38f798c3d4ce->y_bee009126d70c89ce914fe7ac7c3f63a\n\n\n\n\n\nz_ad477f14ef09e2ba3677f10245749435\n\nz=<pyiron_base.project.delayed.DelayedObject object at 0x7f18837cb140>\n\n\n\nx_1d847da32ecaabf6731c38f798c3d4ce->z_ad477f14ef09e2ba3677f10245749435\n\n\n\n\n\ny_bee009126d70c89ce914fe7ac7c3f63a->create_function_job_c4ec5c7a7dd53d01300058ba6c796595\n\n\n\n\n\nz_ad477f14ef09e2ba3677f10245749435->create_function_job_c4ec5c7a7dd53d01300058ba6c796595\n\n\n\n\n\ny_a9ec4f5f33f0d64e74ed5d9900bceac6\n\ny=2\n\n\n\ny_a9ec4f5f33f0d64e74ed5d9900bceac6->x_29b9ebbd9ab08db91ecdc7d0038e5fbc\n\n\n\n\n\ny_a9ec4f5f33f0d64e74ed5d9900bceac6->y_bee009126d70c89ce914fe7ac7c3f63a\n\n\n\n\n\ny_a9ec4f5f33f0d64e74ed5d9900bceac6->z_ad477f14ef09e2ba3677f10245749435\n\n\n\n\n"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"delayed_object.pull()","metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"The job add_x_and_y_68901482a2c5221cc845f828aabebd27 was saved and received the ID: 1\nThe job add_x_and_y_and_z_b671e81aaa4670d81d7eee509650af8d was saved and received the ID: 2\n"},{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"6"},"metadata":{}}],"execution_count":19}]} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Workflow with aiida" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define workflow with aiida" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "from python_workflow_definition.aiida import write_workflow_json\n", + "\n", + "from aiida_workgraph import WorkGraph, task\n", + "from aiida import orm, load_profile\n", + "load_profile()\n", + "\n", + "workflow_json_filename = \"aiida_to_jobflow_simple.json\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "from simple_workflow import add_x_and_y as _add_x_and_y\n", + "from simple_workflow import add_x_and_y_and_z" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "add_x_and_y = task(outputs=['x', 'y', 'z'])(_add_x_and_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "wg = WorkGraph(\"wg-simple\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "add_x_and_y_task = wg.add_task(\n", + " add_x_and_y,\n", + " name=\"add_x_and_y\",\n", + " x=orm.Int(1),\n", + " y=orm.Int(2),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "add_x_and_y_and_z_task = wg.add_task(\n", + " add_x_and_y_and_z,\n", + " name=\"add_x_and_y_and_z\",\n", + " x=add_x_and_y_task.outputs.x,\n", + " y=add_x_and_y_task.outputs.y,\n", + " z=add_x_and_y_task.outputs.z,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'nodes': {'0': 'simple_workflow.add_x_and_y',\n", + " '1': 'simple_workflow.add_x_and_y_and_z',\n", + " '2': 1,\n", + " '3': 2},\n", + " 'edges': [{'tn': 1, 'th': 'x', 'sn': 0, 'sh': 'x'},\n", + " {'tn': 1, 'th': 'y', 'sn': 0, 'sh': 'y'},\n", + " {'tn': 1, 'th': 'z', 'sn': 0, 'sh': 'z'},\n", + " {'tn': 0, 'th': 'x', 'sn': 2, 'sh': None},\n", + " {'tn': 0, 'th': 'y', 'sn': 3, 'sh': None}]}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "write_workflow_json(wg=wg, file_name=workflow_json_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[38;5;238m───────┬────────────────────────────────────────────────────────────────────────\u001b[0m\n", + " \u001b[38;5;238m│ \u001b[0mFile: \u001b[1maiida_to_jobflow_simple.json\u001b[0m\n", + "\u001b[38;5;238m───────┼────────────────────────────────────────────────────────────────────────\u001b[0m\n", + "\u001b[38;5;238m 1\u001b[0m \u001b[38;5;238m│\u001b[0m \u001b[38;5;231m{\u001b[0m\n", + "\u001b[38;5;238m 2\u001b[0m \u001b[38;5;238m│\u001b[0m \u001b[38;5;231m \u001b[0m\u001b[38;5;208m\"\u001b[0m\u001b[38;5;208mnodes\u001b[0m\u001b[38;5;208m\"\u001b[0m\u001b[38;5;231m:\u001b[0m\u001b[38;5;231m \u001b[0m\u001b[38;5;231m{\u001b[0m\n", + "\u001b[38;5;238m 3\u001b[0m \u001b[38;5;238m│\u001b[0m \u001b[38;5;231m \u001b[0m\u001b[38;5;208m\"\u001b[0m\u001b[38;5;208m0\u001b[0m\u001b[38;5;208m\"\u001b[0m\u001b[38;5;231m:\u001b[0m\u001b[38;5;231m \u001b[0m\u001b[38;5;186m\"\u001b[0m\u001b[38;5;186msimple_workflow.add_x_and_y\u001b[0m\u001b[38;5;186m\"\u001b[0m\u001b[38;5;231m,\u001b[0m\n", + "\u001b[38;5;238m 4\u001b[0m \u001b[38;5;238m│\u001b[0m \u001b[38;5;231m 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"trusted": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/geiger_j/.aiida_venvs/adis/lib/python3.10/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.\n", + " \"cipher\": algorithms.TripleDES,\n", + "/home/geiger_j/.aiida_venvs/adis/lib/python3.10/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.\n", + " \"class\": algorithms.TripleDES,\n" + ] + } + ], + "source": [ + "from python_workflow_definition.jobflow import load_workflow_json" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "from jobflow.managers.local import run_locally" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "flow = load_workflow_json(file_name=workflow_json_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-04-02 14:22:25,312 INFO Started executing jobs locally\n", + "2025-04-02 14:22:25,474 INFO Starting job - add_x_and_y (064f2b60-33e7-4cee-b5cd-c35156989e6f)\n", + "2025-04-02 14:22:25,476 INFO Finished job - add_x_and_y (064f2b60-33e7-4cee-b5cd-c35156989e6f)\n", + "2025-04-02 14:22:25,477 INFO Starting job - add_x_and_y_and_z (287a5309-63df-4256-ba6b-4bd56ee20881)\n", + "2025-04-02 14:22:25,479 INFO Finished job - add_x_and_y_and_z (287a5309-63df-4256-ba6b-4bd56ee20881)\n", + "2025-04-02 14:22:25,479 INFO Finished executing jobs locally\n" + ] + }, + { + "data": { + "text/plain": [ + "{'064f2b60-33e7-4cee-b5cd-c35156989e6f': {1: Response(output={'x': 1, 'y': 2, 'z': 3}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/geiger_j/aiida_projects/adis/git-repos/python-workflow-definition'))},\n", + " '287a5309-63df-4256-ba6b-4bd56ee20881': {1: Response(output=6, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/geiger_j/aiida_projects/adis/git-repos/python-workflow-definition'))}}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = run_locally(flow)\n", + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Workflow with pyiron_base" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "from pyiron_base import Project" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "from python_workflow_definition.pyiron_base import load_workflow_json" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1621d094918943178c0a712c147c25af", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/2 [00:00\n", + "\n", + "\n", + "\n", + "\n", + "create_function_job_c7d5a2eaf006faed5e048478186e5006\n", + "\n", + "create_function_job=<pyiron_base.project.delayed.DelayedObject object at 0x71a5dafa0790>\n", + "\n", + "\n", + "\n", + "x_72c4a551025d57f72dca17727aef044e\n", + "\n", + "x=<pyiron_base.project.delayed.DelayedObject object at 0x71a5dafa0100>\n", + "\n", + "\n", + "\n", + "x_72c4a551025d57f72dca17727aef044e->create_function_job_c7d5a2eaf006faed5e048478186e5006\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x_1d847da32ecaabf6731c38f798c3d4ce\n", + "\n", + "x=1\n", + "\n", + "\n", + "\n", + "x_1d847da32ecaabf6731c38f798c3d4ce->x_72c4a551025d57f72dca17727aef044e\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y_454d7f4e4b54021d3386dcdafb7344ee\n", + "\n", + "y=<pyiron_base.project.delayed.DelayedObject object at 0x71a5dafa0130>\n", + "\n", + "\n", + "\n", + "x_1d847da32ecaabf6731c38f798c3d4ce->y_454d7f4e4b54021d3386dcdafb7344ee\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z_7b1d0ad3687984f980950fa9864be0fd\n", + "\n", + "z=<pyiron_base.project.delayed.DelayedObject object at 0x71a5dafa00a0>\n", + "\n", + "\n", + "\n", + "x_1d847da32ecaabf6731c38f798c3d4ce->z_7b1d0ad3687984f980950fa9864be0fd\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y_454d7f4e4b54021d3386dcdafb7344ee->create_function_job_c7d5a2eaf006faed5e048478186e5006\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z_7b1d0ad3687984f980950fa9864be0fd->create_function_job_c7d5a2eaf006faed5e048478186e5006\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y_a9ec4f5f33f0d64e74ed5d9900bceac6\n", + "\n", + "y=2\n", + "\n", + "\n", + "\n", + "y_a9ec4f5f33f0d64e74ed5d9900bceac6->x_72c4a551025d57f72dca17727aef044e\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y_a9ec4f5f33f0d64e74ed5d9900bceac6->y_454d7f4e4b54021d3386dcdafb7344ee\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y_a9ec4f5f33f0d64e74ed5d9900bceac6->z_7b1d0ad3687984f980950fa9864be0fd\n", + "\n", + "\n", + "\n", + "\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "delayed_object = load_workflow_json(project=pr, file_name=workflow_json_filename)\n", + "delayed_object.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job add_x_and_y_68901482a2c5221cc845f828aabebd27 was saved and received the ID: 2\n", + "The job add_x_and_y_and_z_b671e81aaa4670d81d7eee509650af8d was saved and received the ID: 3\n" + ] + }, + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delayed_object.pull()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}